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August 8, 2022

Using Behavioral Biometrics for User Authentication as added security measures – Advantages and Disadvantages

Filed under: Article Releases,Computer Security,My Thoughts — Suramya @ 11:59 PM

In this paper we explore how users can be uniquely identified using biometrics other than fingerprints, facial recognition, iris recognition etc on a continuous basis. We explore the use to techniques such as typing style, computer use style to see if we can create a model to uniquely identify a user based on the way they type and use the computer. As this method allows a system to constantly reauthenticate a user based on characteristics that are almost impossible to fake we look at the complexity of how this can be integrated as a security measure for secure systems. We also look at the pros and cons of implementing this authentication mechanism and explore potential problems this system generates for the user and administrators. Specifically, we look at how the system would deal with users who are sick, under medication or stress that could change their usage patterns and is it worth the expense and privacy issues to implement such a system.

Introduction and background

User authentication is the process of verifying the identify of a user or process trying to access a system, online service, connected device, infrastructure resources etc. Traditionally authentication is done by having the user provide one or more of the following:

  • Something they know
  • Something they have
  • Something they are

Let’s look at each of these one by one. The oldest way of authentication to computer systems is using usernames and passwords. The first password protection system was implemented in 1961 by Fernando J. Corbató at MIT (Workos, 2020). This allowed the system to identify users based on a secret password that only they knew. The first set of passwords were stored in plain text, but then password encryption was implemented so that users could not read the passwords for other users.

However, passwords can be leaked or guessed. In the past few years there have been major leaks of authentication data which have been decrypted and sophisticated password crackers have been created that can crack passwords based on dictionary attacks and brute force attacks. To safeguard against this attack vector another authentication mechanism was created that authenticates users based on something they have with them. This can include hardware keys, smartcards etc and these hardware devices would contain an embedded certificate that can be used to uniquely identify the holder.

The final method of authentication is something you are, which is provided by Biometric authentication. Some of the biometric methods that can be used are fingerprints, hand geometry, retinal or iris scans, face scans, and voice analysis. Fingerprints, Face Scans and iris scans are the most widely used biometric method in use today.

Multifactor Authentication
When a system uses a combination of one or more of the authentication methods described in the previous section the system is said to be using Multi-factor Authentication (MFA). The key point to remember is that a system is only considered to be using MFA if the authentication factors are in at least two of the categories. So if the authentication mechanism uses a password and a second pin to authenticate, it won’t count as MFA because both are things that you know.

Weaknesses in the current User Authentication methods

The current user authentication methods have several weaknesses that make it easy for attackers to compromise and bypass the checks. Complex passwords are harder to crack or guess than simple passwords, but they are harder for users to remember. So, users tend to use the same passwords across multiple sites or use passwords that are simple to remember. Unfortunately, passwords that are simple to remember are also easy to guess.
Another risk is that an attacker can compromise a site or server using vulnerabilities in the OS, services or applications running on it. Once they have access, they can gain access to the stored passwords for all users and depending on the encryption scheme used the passwords for user accounts can be guessed quickly. This is an attack vector that has been seen frequently over the past few years with password lists for major sites such as LinkedIn (Morris, 2021) and Yahoo (Goel & Perlroth, 2016) etc being compromised and leaked.

Hardware tokens or smart cards can be cloned, copied or stolen. If the card is not deactivated when it is lost or stolen an attacker can use it to gain access to restricted resources. Tools to create copies of smartcards are available easily in the market (Benchoff, 2016) using which an attacker can clone the cards quickly.

Biometrics was touted as an authentication mechanism that is almost impossible to bypass but unfortunately the hype didn’t match reality. Fingerprint authentication systems have been compromised using copies of fingerprints lifted from glasses, door knobs etc transferred to jello, Glycerin and gelatin. (Barral & Tria, 2009)

Facial recognition systems have been fooled by photographs and cosmetics. Researchers have also used the StyleGAN Generative Adversarial Network (GAN) to create master faces that can be used to impersonate 40% of the population. (Shmelkin et al., 2021)

Voice authentication systems have been bypassed using voice recordings and AI based ‘deep fake’ technologies. Amazon recently showcased technology that allows Alexa to impersonate the voices of people based on a few minutes long voice recording of the person being impersonated.

Similar bypasses have been found for all authentication mechanisms in use currently and thus researchers have been exploring new authentication mechanisms which would be harder to bypass and fool. One such field being explored in behavioral biometrics and we will explore the field, it’s implications, the pros and cons of the tech in this paper.

Introduction to Behavioral Biometrics

Behavioral biometrics is the study and use of uniquely identifying and measurable patterns in human activities that can include keystroke dynamics, gait analysis, mouse use characteristics, signature analysis etc. The field postulates that a user can be identified based on these characteristics just as uniquely as they can be using physical biometrics.

Another advantage of using Behavioral Biometrics over physical biometrics is that it doesn’t require specialized equipment to collect the data. Data can be collected using existing hardware and only requires software analysis and processing which makes it cheaper to implement to a certain extent and we will look at this in more detail later in the paper.

Behavioral Biometrics can include the following:

Keystroke Dynamics:

According to the studies, if a group of users is asked to type the paragraph of text, each of them will type the text slightly differently with different delays between each character being typed, and different rhythms for the text. This allows a system to identify the user based on how they type including criteria such as:

  • The user’s typing speed
  • Time elapsed between each consecutive keystroke
  • The time that each key is held down
  • The frequency with which the number pad keys are used
  • The timing and sequence of the keys used to type a capital letter
  • The Error Rate in typing, such as using the Backspace keys and words repeatedly mistyped by the user.

As each person would type the password slightly differently the system can use it to identify the authorized user and block attackers who might have gained the password for a given user.

Cursor Movement:

This uses the tracking speed, clicks and path taken by the mouse cursor movement during use to create a profile for the active user. This would be useful if the user uses the same set of applications frequently, if they are using a varied set of applications that keep changing then this would not be accurate.

Finger pressure on keypad:

This analyses the pressure on the keyboard to create a user profile. This is a lot more relevant for mobile devices and other devices with a touchscreen interface as the allow us to capture pressure details easily without extra hardware.

Posture:
Every person has a different way of standing and a sufficiently trained system can look for differences in how the person sits in front of the computer and their posture while using the system.

Gait:

Gait analysis attempts to identify a person based on their walking style, which includes movements such as stride length, posture, and speed of travel etc.

Each of the methods we listed above can potentially be used to continuously re-validate a logged in user.

Historical use of Behavioral Biometrics for authentication

Historically, behavioral biometrics have been in use since the 1860s when experienced telegraph operators were able to identify individual operators by the way they would send the signals. In World war II allied officers used it to validate the authenticity of messages they received based on how they were sent. (Das, 2020) Similarly, other organizations used this ability as well as an extra validation layer when communicating instructions over telegraph.

The Military has also used gait recognition to identify imposters in their base who are trying to impersonate authorized personnel to gain access to sensitive information.

Current state & the Future for Behavioral Biometrics

The behavioral biometrics market revenue totaled ~US$ 1.1 Bn in 2020, according to Future Market Insights (FMI). The overall market is expected to reach ~US$ 11.2 Bn by 2031, growing at a CAGR of 23.6% for 2021 – 31. (Future Market Insights, 2021)

As we can see, an increasing number of institutes, financial companies, website owners are using behavioral biometrics in their systems to detect fraudulent usage. The Royal Bank of Scotland uses it to monitor visitors to their websites and apps, others use it in their applications to monitor and authenticate users as an extra verification layer. (PYMNTS.com, 2018)

With the increase in processing capacity, sensor sensitivity and processing algorithms systems can make more accurate identifications of individual users. This allows systems to detect bots, password sharing/compromise.

Ecommerce sites have increasingly started incorporating this technology into their setup to prevent fraud. It can also potentially allow systems to make an educated judgment about the visitor’s gender and age to show appropriate products.

Considering the advantages and minimal hardware investment we will only see an increase in the use of Behavioral Biometrics for authentication in the future.

Advantages of using Behavioral Biometrics for authentication

Behavioral Biometrics have the following advantages that make them attractive for companies and institutes to implement:

  • Flexibility: The data being analyzed is not limited to currently identified sets that we have discussed so far. Since most of the processing being done is on the software side the organization can easily add additional behavioral data to be analyzed and processed.
  • Convenience: This a major plus point for the technology is that it is a passive layer of security. This allows it to work without interfering with the user workflows. This removes a major obstacle in incorporating security into the system as the traditional security setups decrease the usability of the system.
  • Efficiency: They can be applied in real-time to detect fraudulent use and the system can be run against historic data as well to detect improper use after fact.
  • Security: Behavioral characteristics are hard to replicate and thus incorporating this additional layer of security improves the security of the system.

Disadvantages of using Behavioral Biometrics for authentication

As with all systems there are some disadvantages of using a Behavioral Biometric system for authentication as well. If we are using the Keystroke analysis then the text being entered has to be long enough for the system to generate a profile and match it so if we are only using it as an additional validation step during password entry and the user’s password is too short, then the system might not be able to create and match a profile.

Another problem is that a user’s behavior can change drastically due to various valid reasons and that can cause access issues when the algorithm is unable to account for the changes. Some of the reasons can include:

  • Illness or Injury: If a person is injured or unwell then their usage patternswill change
  • Stress
  • Pregnancy
  • Sleep deficiency
  • Caffeine deficiency or overindulgence
  • Tiredness: If a user logs back in after a session in the gym their usage patterns are going to differ from the pattern before their gym session
  • Time of day: Some people are more active during certain times of day so their usage patterns will vary based on the time of the day.
  • Distractions: If the user is distracted while working , or example, if they are on a call and working at the same time. Their behavior patterns will be different.
  • Location: If the person logs in from a different location and are working with a different setup their metrics are going to be different. For example the profile when using an egronomic keyboard in office vs using a laptop keyboard while working remotely will be drasticly different and the system will have a hard time creating a consolidated profile for such users.

Another major issue with this technology is the Privacy implications. If we are implementing a system that monitors every keystroke and mouse movement and logs it for analysis then that has a serious privacy implication as sensitive data that shouldn’t be logged such as medical information, personal account passwords, other sensitive information etc can get logged as well. Once the data is logged there is a possibility of data leaks or a breach of the security system which would expose the collected information to an attacker.

Depending on the user’s location collection of this kind of data can be illegal due to rules such as the GDPR (Krausová, 2018), the California Consumer Privacy Act (CCPA) and other such rules. They will also limit the information that can be transmitted across state & country boundaries which can be a concern for multinational companies.

Finally incorporating the processing required for behavior analysis on the local system can be resource intensive which might make the setup infeasible for older machines. If the processing of the data is consolidated at a central location then the usage data would need to be transmitted to the location over the network that can potentially max out the bandwidth and depending on network congestion cause unacceptable delays in the processing and access.

Results and Recommendations

Based on our review of the current state of Behavioral Biometrics in the industry and the technological state of the system/algorithms we find that the technology does help increase the security of the system by adding an additional layer of security to the system. However, it is not yet mature enough to deploy for general commercial implementation and should only be used for securing highly sensitive systems and infrastructure where the security considerations outweigh the limitations identified earlier in the paper.
Once the technology is more mature and the issues identified earlier have been mitigated it can slowly be incorporated in the general computing world as an optional additional layer of security. At no point should this be used as the only layer of security for any system.

Conclusion

Behavioral Biometrics as a security measure is a technology still in its early stages of use and implementation and while it does add an additional layer of security the current limitations do not justify a general release and implementation in general use computing. The system should only be implemented in systems such as classified military systems, critical corporate servers containing highly sensitive information etc where the benefits or security concerns outweigh the disadvantages of using a technology that still needs to mature more.
 
References

Alzubaidi, A., & Kalita, J. (2016). Authentication of smartphone users using behavioral biometrics. IEEE Communications Surveys & Tutorials, 18(3), 1998–2026. https://doi.org/10.1109/comst.2016.2537748

Araujo, L. C. F., Sucupira, L. H. R., Lizarraga, M. G., Ling, L. L., & Yabu-Uti, J. B. T. (2005). User authentication through typing biometrics features. IEEE Transactions on Signal Processing, 53(2), 851–855. https://doi.org/10.1109/tsp.2004.839903

Banerjee, S. P., & Woodard, D. (2012). Biometric authentication and identification using Keystroke Dynamics: A survey. Journal of Pattern Recognition Research, 7(1), 116–139. https://doi.org/10.13176/11.427

Barral, C., & Tria, A. (2009). Fake fingers in fingerprint recognition: Glycerin supersedes gelatin. Formal to Practical Security, 57–69. https://doi.org/10.1007/978-3-642-02002-5_4

Benchoff, B. (2016, January 18). Emulating and cloning smart cards. Hackaday. Retrieved June 27, 2022, from https://hackaday.com/2016/01/18/emulating-and-cloning-smart-cards/

Bo, C., Zhang, L., Li, X.-Y., Huang, Q., & Wang, Y. (2013). Silentsense. Proceedings of the 19th Annual International Conference on Mobile Computing & Networking – MobiCom ’13. https://doi.org/10.1145/2500423.2504572

Das, R. (2020, October 14). A behavioral biometric – keystroke recognition. A Behavioral Biometric – Keystroke Recognition. https://resources.infosecinstitute.com/topic/a-behavioral-biometric-keystroke-recognition/
Future Market Insights. (2021, October). Behavioral biometrics market. Future Market Insights. https://www.futuremarketinsights.com/reports/behavioral-biometrics-market

Goel, V., & Perlroth, N. (2016, December 14). Yahoo says 1 billion user accounts were hacked. The New York Times. https://www.nytimes.com/2016/12/14/technology/yahoo-hack.html

Krausová, A. (2018). Online behavior recognition: Can we consider it biometric data under GDPR? Masaryk University Journal of Law and Technology, 12(2), 161–178. https://doi.org/10.5817/mujlt2018-2-3

Morris, C. (2021, June 30). LinkedIn data theft exposes personal information of 700 million people. Fortune. https://fortune.com/2021/06/30/linkedin-data-theft-700-million-users-personal-information-cybersecurity/

PYMNTS.com. (2018, August 15). What’s behind the rise of behavioral biometrics? PYMNTS.com. Retrieved June 27, 2022, from https://www.pymnts.com/fraud-prevention/2018/behavioral-biometrics-uk-banks-authentication-security-privacy/

Shmelkin, R., Friedlander, T., & Wolf, L. (2021). Generating master faces for dictionary attacks with a network-assisted Latent Space evolution. 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). https://doi.org/10.1109/fg52635.2021.9666968

Workos. (2020, September 5). A developer’s history of authentication – WorkOS. A Developer’s History of Authentication. https://workos.com/blog/a-developers-history-of-authentication

Note: This was originally written as a paper for one of my classes at EC-Council University in Q2 2022.

– Suramya

April 29, 2022

Malware in Windows: TPM Bypasses & Firmware level persistence

Filed under: Article Releases,Computer Security,Computer Software,My Thoughts,Tech Related — Suramya @ 7:13 AM

Malware is the short form for Malicious Software and is basically software that allows attackers to infect a computer system or device to steal information, disrupt operations or gain access to sensitive data. It is a general term that includes viruses, worms, trojans, spyware, rootkits etc. (Cisco, 2021)

Conceptually the foundations for creating malware were laid almost simultaneously with the creation of the first computers. In 1951, John von Neumann proposed methods on how to create self-replicating automata (Neumann, 1951) and a few years later in 1959 Lionel Penrose published his paper on ‘Self-Reproducing Machines’ this paper was used as the basis for creating replicating machine code that were the basis of the later generations of malware. In 1970’s the creeper virus infected the ARPANET (Milošević, 2013) followed shortly after by Rabbit (Milošević, 2013) which spread rapidly to computers and created copies of itself overloading the machine and impacting system performance. (Milošević, 2013)

In the 1986, the first malware called Brain.A that targeted the PC platform was released. (Milošević, 2013) It used floppy disks as the infection mechanism by infecting the boot sector of every floppy disk used in an infected computer. Other viruses of the time used similar mechanisms to propagate and were quite prevalent by the measures of the time. Once Microsoft Windows was released viruses were created that targeted the new operating system with WinVir being the first virus for the new operating system, it gained persistence by modifying the Windows Executable files. (Milošević, 2013) It spread to new systems over floppy disks.
For almost a decade, infected disks and CD’s remained the primary method of infection for computers. In 1998 this changed with the release of Happy99 in late 1998 that spread via email attachments. Another popular vector for virus infections was macro viruses that infected Microsoft word files which were shared frequently with other users allowing the virus to spread. With the increasing popularity of the Internet, the new malware created during this time leveraged the internet as a transmission vector.

In early 2000, Code Red worm was created that leveraged vulnerabilities in the IIS webservers to propagate. (Milošević, 2013) This opened a new infection vector where the malware would scan for and exploit systems running vulnerable software.

Over the years, malware has become more and more common and has evolved to gain persistence using multiple methods such as using rootkits to infect the OS kernel and other such methods. The one constant throughout the years was that we could clean up a malware infection by formatting the infected drive and restoring from a clean backup. As long as the backup and the installation media were clean we could be confident that the infection was cleared.

Unfortunately, this is no longer the case with new strains of malware using sophisticated techniques to gain persistence using the computer firmware.

A. UEFI malware – The early years

UEFI rootkits were referenced in various leaks and were considered mostly theoretical. The Hacking Team referenced something called ‘rkloader’ in their internal presentations and the Vault7 leaks referenced ‘DerStarke’ which was an EFI/UEFI boot implant. But there was no real evidence of these being used so they were considered mostly theoretical for the most part.

This changed in 2018 when the first rootkit that leveraged the UEFI to achieve persistence was discovered. This malware called Lojax was created by the Sednit APT group. It used a malicious UEFI module written into the SPI flash memory to ensure that it was able to execute malware during the boot up process. (ESET Research, 2018)

B. UEFI Malware – Infecting SPI flash memory

The LoJax malware used the kernel driver RwDrv.sys to access the UEFI settings. The driver is distributed with RWEverything, a freeware utility that can read the BIOS information in most computers. (ESET Research, 2018)

The malware used this driver to read the contents of the SPI flash memory into a file, by running a file called ReWriter_binary.exe. The data in the SPI is stored in volumes using the Firmware File System (FFS). It then parses the volues to search for the Ip4Dxe file. This file along with DXE Core is then modified to add the malicious UEFI module to it post which the entire file is written back to the SPI memory. If the configuration allows write access to SPI the malware immediately writes to the SPI memory but if write access is disabled it exploited a race condition vulnerability in the BIOS locking mechanism to bypass the write protection in SPI flash memory. (CERT, 2015)

C. MoonBounce: UEFI Bootkit

The MoonBounce Bootkit is the third instance of malware that uses UEFI to gain persistence, with Lojax and MosaicRegressor being the other two instances where it was used.

MoonBounce is a lot more sophisticated than the previous iterations and it executes completely in the system memory without writing anything to the hard drive making it a lot harder to detect than the previous iterations of the malware. It stages the execution and deployment of payloads over the internet allowing the attacker to deploy payloads on the system to achieve specific tasks.
MoonBounce was detected in spring 2021 and like the previous iterations attacks the DXE Core module in UEFI to infect the SPI Memory.

D. Using TPM Module & Trusted Computing to protect against this attack

The TPM Module in the modern machines is designed to provide hardware-based, security-related functions and allows the system to secure the system using integrated cryptographic keys.

If TPM is enabled and is being used correctly then it gives the system a way to ensure that all firmware and boot files are unmodified. If any of the files are modified then they will not pass the cryptographic check and the boot process will be halted. This would prevent the infected SPI memory from being loaded and would warn the defenders that their system has been breached.

Unfortunately, it is possible to disable the TPM chip for historical compatibility reasons, so the malware can do the same. One of the ways to disable the check and bypass the Secure Boot & TPM check is to modify the registry files in Windows. The steps to do so are very simple and are shown below (Tibbetts, 2021):

  • At the run prompt type in regedit, and press Enter.
  • Go to Computer\HKEY_LOCAL_MACHINE\SYSTEM\Setup
  • Right-click on Setup and click New > Key. Name that LabConfig
  • Click on LabConfig, then right-click on the right pane, and click New > DWORD (32-bit Value).
  • Name the entry as BypassTPMCheck and change its Value data to 1
  • Create two more DWORDS and change the Value data to 1 just like you did above and name them BypassRAMCheck and BypassSecureBootCheck.

This removes the check for Secure Boot and while it can be desired at times it does open up the system to risk so should only be used for specific use cases where no other option is available.

Protecting against malware using firmware level persistence

To protect against this threat, we need to ensure that all components of the operating system and software on the computer are patched and updated to the latest version. We should enable end-point monitoring and IDS on the network to detect infection attempts. This will allow us to detect the malware before it infects the system and block it pre-emptively. The internet and email gateways should scan all incoming files to detect and block malware. In addition to the standard precautions to protect against malware, we should also ensure that all systems on the network are running the latest version of the UEFI/BIOS available.

Unfortunately, the remediation of the security issues in UEFI is a hard problem and doesn’t have an easy solution. So, the best way to protect against the threat is to try to prevent the system from getting infected in the first place.

Another option to detect infected SPI Memory is to create a tool that periodically creates a dump of the SPI memory and compares the checksum of the dump with a known clean dump. If the values don’t match then there is a high probability that the memory is infected and the administrators can then take steps to clean the firmware by flashing it with a known clean version of the firmware.

With the new methods of persistence available to the malware writers the best way to protect the assets is to try to ensure that you prevent the infection from happening in the first place. Once the machine is infected the task becomes harder and we would need to spend extra time and effort to clean and restore the systems to a clean state.
Done correctly this will decrease the risk of data exfiltration but no technique to detect infection is perfect so a lot of review and audits need to be done on a periodic basis to ensure that the system is still secure.
 

References

CERT. (2015, January 5). CERT/CC Vulnerability note vu#766164. VU#766164 – Intel BIOS locking mechanism contains race condition that enables write protection bypass. Retrieved March 21, 2022, from https://www.kb.cert.org/vuls/id/766164

Cisco. (2021, July 30). What is malware? – definition and examples. Cisco. Retrieved March 21, 2022, from https://www.cisco.com/c/en_in/products/security/advanced-malware-protection/what-is-malware.html
ESET Research. (2018, October 9). Lojax: First UEFI rootkit found in the wild, courtesy of the Sednit Group. WeLiveSecurity. Retrieved March 21, 2022, from https://www.welivesecurity.com/2018/09/27/lojax-first-uefi-rootkit-found-wild-courtesy-sednit-group/

Neumann, J. V. (1951). Massachusetts Institute of Technology. Theory of Self Replicating Automata. Retrieved March 21, 2022, from https://cba.mit.edu/events/03.11.ASE/docs/VonNeumann.pdf
Tibbetts, T. (2021, July 10). How to bypass secure boot & trusted platform module. Providing Free and Editor Tested Software Downloads. Retrieved March 21, 2022, from https://www.majorgeeks.com/content/page/bypass_tpm.html.

This was a paper for my Class in Q1 2022 which is why it is more formal than my usual posts.

April 22, 2022

Implications and Impact of Quantum Computing on Existing Cryptography

Filed under: Article Releases,Computer Security,Computer Software,Emerging Tech,My Thoughts,Quantum Computing,Tech Related — Suramya @ 11:59 PM

As all of you are aware the ability to break encryption of sensitive data like financial systems, private correspondence, government systems in a timely fashion is the holy grail of computer espionage. With the current technology it is unfeasible to break the encryption in a reasonable timeframe. If the target is using a 256-bit key an attacker will need to try a max of 2256 possible combinations to brute-force it. This means that even with the fastest supercomputer in the world will take millions of years to try all the combinations (Nohe, 2019). The number of combinations required to crack the encryption key increase exponentially, so a 2048-bit key has 22048 possible combinations and will take correspondingly longer time to crack. However, with the recent advances in Quantum computing the dream of breaking encryption in a timely manner is close to becoming reality in the near future.

Introduction to Quantum Computing

So, what is this Quantum computing and what makes it so special? Quantum computing is an emerging technology field that leverages quantum phenomena to perform computations. It has a great advantage over conventional computing due to the way it stores data and performs computations. In a traditional system information is stored in the form of bits, each of which can be either 0 or 1 at any given time. This makes a ‘bit’ the fundamental using of information in traditional computing. A Quantum computer on the other hand uses a ‘qubit’ as its fundamental unit and unlike the normal bit, a qubit can exist simultaneously as 0 and 1 — a phenomenon called superposition (Freiberger, 2017). This allows a quantum computer to act on all possible states of a qubit simultaneously, enabling it to perform massive operations in parallel using only a single processing unit. In fact, a theoretical projection has postulated that a Quantum Computer could break a 2048-bit RSA encryption in approximately 8 hours (Garisto, 2020).

In 1994 Peter W. Shor of AT&T deduced how to take advantage of entanglement and superposition to find the prime factors of an integer (Shor, 1994). He found that a quantum computer could, in principle, accomplish this task much faster than the best classical calculator ever could. He then proceeded to write an algorithm called Shor’s algorithm that could be used to crack the RSA encryption which prompted computer scientists to begin learning about quantum computing.

Introduction to Current Cryptography

Current security of cryptography relies on certain “hard” problems—calculations which are practically impossible to solve without the correct cryptographic key. Just as it is easy to break a glass jar but difficult to stick it back together there are certain calculations that are easy to perform but difficult to reverse. For example, we can easily multiply two numbers to get the result, however it is very hard to start with the result and work out which two numbers were multiplied to produce it. This becomes even more hard as the numbers get larger and this forms the basis of algorithms like the RSA (Rivest et al., 1978) that would take the best computers available billions of years to solve and all current IT security aspects are built on top of this basic foundation.

There are multiple ways of classifying cryptographic algorithms but in this paper, they will be classified based on the keys required for encryption and decryption. The main types of cryptographic algorithms are symmetric cryptography and asymmetric cryptography.

Symmetric Cryptography

Symmetric cryptography is a type of encryption that uses the same key for both encryption and decryption. This requires the sender and receiver to exchange the encryption key securely before encrypted data can be exchanged. This type of encryption is one of the oldest in the world and was used by Julius Caesar to protect his communications in Roman times (Singh, 2000). Caesar’s cipher, as it is known is a basic substitution cypher where a number is used to offset each alphabet in the message. For example, if the secret key is ‘4’ then each alphabet would be replaced with the 4th letter down from it, i.e. A would be replaced with E, B with F and so on. Once the sender and receiver agree on the encryption key to be used, they can start communicating. The receiver would take each character of the message and then go back 4 letters to arrive at the plain-text message. This is a very simple example, but modern cryptography is built on top of this principle.

Another example is from world war II during which the Germans were encrypting their transmissions using the Enigma device to prevent the Allies from decrypting their messages as they had in the first World War (Rijmenants, 2004). Each day both the receiver and sender would configure the gears and specific settings to a new value as defined by secret keys distributed in advance. This allowed them to transmit information in an encrypted format that was almost impossible for the allied forces to decrypt. Examples of symmetric encryption algorithms include Advanced Encryption Standard (AES), Data Encryption Standard (DES), and International Data Encryption Algorithm (IDEA).

Symmetric encryption algorithms are more efficient than asymmetric algorithms and are typically used for bulk encryption of data.

Asymmetric Cryptography

Unlike symmetric cryptography asymmetric cryptography uses two keys, one for encryption and a second key for decryption (Rouse et al., 2020). Asymmetric cryptography was created to address the problems of key distribution in symmetric encryption and is also known as public key cryptography. Modern public key cryptography was first described in 1976 by Stanford University professor Martin Hellman and graduate student Whitfield Diffie. (Diffie & Hellman, 1976)

Asymmetric encryption works with public and private keys where the public key is used to encrypt the data and the private key is used to decrypt the data (Rouse et al., 2020). Before sharing data, a user would generate a public-private keypair and they would then publish their public key on their website or in key management portals. Now, whoever wants to send private data to them would use their public key to encrypt the data before sending it. Once they receive the cipher-text they would use their private key to decrypt the data. If we want to add another layer of authentication to the communication, the sender would encrypt the data with their private key first and then do a second layer of encryption using the recipient’s public key. The recipient would first decrypt the message using their private key, then decrypt the result using the senders public key. This validates that the message was sent by the sender without being tampered. Public key cryptography algorithms in use today include RSA, Diffie-Hellman and Digital Signature Algorithm (DSA).

Quantum Computing vs Classical Computing

Current state of Quantum Computing

Since the early days of quantum computing we have been told that a functional quantum computer is just around the corner and the existing encryption systems will be broken soon. There has been significant investment in the field of Quantum computers in the past few years, with organizations like Google, IBM, Amazon, Intel and Microsoft dedicating a significant amount of their R&D budget to create a quantum computer. In addition, the European Union has launched a Quantum Technologies Flagship program to fund research on quantum technologies (Quantum Flagship Coordination and Support Action, 2018).

As of September 2020, the largest quantum computer is comprised of 65 qubits and IBM has published a roadmap promising a 1000 qbit quantum computer by 2023 (Cho, 2020). While this is an impressive milestone, we are still far away from a fully functional general use quantum computer. To give an idea of how far we still have to go Shor’s algorithm requires 72k3 quantum gates to be able to factor a k bits long number (Shor, 1994). This means in order to factor a 2048-bit number we would need a 72 * 20483 = 618,475,290,624 qubit computer which is still a long way off in the future.

Challenges in Quantum Computing

There are multiple challenges in creating a quantum computer with a large number of qubits as listed below (Clarke, 2019):

  • Qubit quality or loss of coherence: The qubits being generated currently are useful only on a small scale, after a particular no of operations they start producing invalid results.
  • Error Correction at scale: Since the qubits generate errors at scale, we need algorithms that will compensate for the errors generated. This research is still in the nascent stage and requires significant effort before it will be ready for production use.
  • Qubit Control: We currently do not have the technical capability to control multiple qubits in a nanosecond time scale.
  • Temperature: The current hardware for quantum computers needs to be kept at extremely cold temperatures making commercial deployments difficult.
  • External interference: Quantum computes are extremely sensitive to interference. Research at MIT has found that ionizing radiation from environmental radioactive materials and cosmic rays can and does interfere with the integrity of quantum computers.

Cryptographic algorithms vulnerable to Quantum Computing

Symmetric encryption schemes impacted

According to NIST, most of the current symmetric cryptographic algorithms will be relatively safe against attacks by quantum computer provided a large key is used (Chen et al., 2016). However, this might change as more research is done and quantum computers come closer to reality.

Asymmetric encryption schemes impacted

Unlike symmetric encryption schemes most of the current public key encryption algorithms are highly vulnerable to quantum computers because they are based on the previously mentioned factorization problem and calculation of discrete logarithms and both of these problems can be solved by implementing Shor’s algorithm on a quantum computer with enough qubits. We do not currently have the capability to create a computer with the required number of qubits due to challenges such as loss of qubit coherence due to ionizing radiation (Vepsäläinen et al., 2020), but they are a solvable problem looking at the ongoing advances in the field and the significant effort being put in the field by companies such as IBM and others (Gambetta et al., 2020).

Post Quantum Cryptography

The goal of post-quantum cryptography is to develop cryptographic algorithms that are secure against quantum computers and can be easily integrated into existing protocols and networks.

Quantum proof algorithms

Due to the risk posed by quantum computers, the National Institute of Standards and Technology (NIST) has been examining new approaches to encryption and out of the initial 69 submissions received three years ago, the group has narrowed the field down to 15 finalists and has now begun the third round of public review of the algorithms (Moody et al., 2020) to help decide the core of the first post-quantum cryptography standard. They are expecting to end the round with one or two algorithms for encryption and key establishment, and one or two others for digital signatures (Moody et al., 2020).

Quantum Key Distribution

Quantum Key Distribution (QKD) uses the characteristics of quantum computing to implement a secure communication channel allowing users to exchange a random secret key that can then be used for symmetrical encryption (IDQ, 2020). QKD solves the problem of secure key exchange for symmetrical encryption algorithms and it has the capability to detect the presence of any third party attempting to eavesdrop on the key exchange. If there is an attempt by a third-party to eavesdrop on the exchange, they will create anomalies in the quantum superpositions and quantum entanglement which will alert the parties to the presence of an eavesdropper, at which point the key generation will be aborted (IDQ, 2020). The QKD is used to only produce and distribute an encryption key securely, not to transmit any data. Once the key is exchanged it can be used with any symmetric encryption algorithm to transmit data securely.

Conclusion

Development of a quantum computer may be 100 years off or may be invented in the next decade, but we can be sure that once they are invented, they will change the face of computing forever including the field of cryptography. However, we should not panic as this is not the end of the world as the work on quantum resistant algorithms is going much faster than the work on creating a quantum computer. The world’s top cryptographic experts have been working on Quantum safe encryption for the past three years and we are nearing the completion of the world’s first post-quantum cryptography standard (Moody et al., 2020). Even if the worst happens and it is not possible to create a quantum safe algorithm immediately, we still have the ability to encrypt and decrypt data using one-time pads until a safer alternative or a new technology is developed.
 
References

Chen, L., Jordan, S., Liu, Y.-K., Moody, D., Peralta, R., Perlner, R., & Smith-Tone, D. (2016). Report on Post-Quantum Cryptography. https://doi.org/10.6028/nist.ir.8105

Cho, A. (2020, September 15). IBM promises 1000-qubit quantum computer-a milestone-by 2023. Science. https://www.sciencemag.org/news/2020/09/ibm-promises-1000-qubit-quantum-computer-milestone-2023.

Clarke, J. (2019, March). An Optimist’s View of the Challenges to Quantum Computing. IEEE Spectrum: Technology, Engineering, and Science News. https://spectrum.ieee.org/tech-talk/computing/hardware/an-optimists-view-of-the-4-challenges-to-quantum-computing.

Diffie, W., & Hellman, M. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644–654. https://doi.org/10.1109/tit.1976.1055638

Freiberger, M. (2017, October 1). How does quantum computing work? https://plus.maths.org/content/how-does-quantum-commuting-work.

Gambetta, J., Nazario, Z., & Chow, J. (2020, October 21). Charting the Course for the Future of Quantum Computing. IBM Research Blog. https://www.ibm.com/blogs/research/2020/08/quantum-research-centers/.

Garisto, D. (2020, May 4). Quantum computers won’t break encryption just yet. https://www.protocol.com/manuals/quantum-computing/quantum-computers-wont-break-encryption-yet.

IDQ. (2020, May 6). Quantum Key Distribution: QKD: Quantum Cryptography. ID Quantique. https://www.idquantique.com/quantum-safe-security/overview/quantum-key-distribution/.
Moody, D., Alagic, G., Apon, D. C., Cooper, D. A., Dang, Q. H., Kelsey, J. M., Yi-Kai, L., Miller, C., Peralta, R., Perlner R., Robinson A., Smith-Tone, D., & Alperin-Sheriff, J. (2020). Status report on the second round of the NIST post-quantum cryptography standardization process. https://doi.org/10.6028/nist.ir.8309

Nohe, P. (2019, May 2). What is 256-bit encryption? How long would it take to crack? https://www.thesslstore.com/blog/what-is-256-bit-encryption/.
Quantum Flagship Coordination and Support Action (2018, October). Quantum Technologies Flagship. https://ec.europa.eu/digital-single-market/en/quantum-technologies-flagship

Rijmenants, D. (2004). The German Enigma Cipher Machine. Enigma Machine. http://users.telenet.be/d.rijmenants/en/enigma.htm.

Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120–126. https://doi.org/10.1145/359340.359342

Rouse, M., Brush, K., Rosencrance, L., & Cobb, M. (2020, March 20). What is Asymmetric Cryptography and How Does it Work? SearchSecurity. https://searchsecurity.techtarget.com/definition/asymmetric-cryptography.

Shor, P. w. (1994). Algorithms for quantum computation: discrete logarithms and factoring. Proceedings 35th Annual Symposium on Foundations of Computer Science, 124–134. https://doi.org/10.1109/sfcs.1994.365700

Singh, S. (2000). The code book: The science of secrecy from Egypt to Quantum Cryptography. Anchor Books.

Vepsäläinen, A. P., Karamlou, A. H., Orrell, J. L., Dogra, A. S., Loer, B., Vasconcelos, F., David, K. K., Melville A. J., Niedzielski B. M., Yoder J. L., Gustavsson, S., Formaggio J. A., VanDevender B. A., & Oliver, W. D. (2020). Impact of ionizing radiation on superconducting qubit coherence. Nature, 584(7822), 551–556. https://doi.org/10.1038/s41586-020-2619-8

Note: This was originally written as a paper for one of my classes at EC-Council University in Q4 2020, which is why the tone is a lot more formal than my regular posts.

– Suramya

April 13, 2022

Internet of Things (IoT) Forensics: Challenges and Approaches

Filed under: Article Releases,Computer Related,Computer Security,Emerging Tech,My Thoughts,Tech Related — Suramya @ 6:18 AM

Internet of Things or IoT consists of interconnected devices that have sensors and software, which are connected to automated systems to gather information and depending on the information collected various actions can be performed. It is one of the fastest growing markets, with enterprise IoT spending to grow by 24% in 2021 from $128.9 billion. (IoT Analytics, 2021).

This massive growth brings new challenges to the table as administrators need to secure IoT devices in their network to prevent them from being security threats to the network and attackers have found multiple ways through which they can gain unauthorized access to systems by compromising IoT systems.

IoT Forensics is a subset of the digital forensics field and is the new kid on the block. It deals with forensics data collected from IoT devices and follows the same procedure as regular computer forensics, i.e., identification, preservation, analysis, presentation, and report writing. The challenges of IoT come into play when we realize that in addition to the IoT sensor or device we also need to collect forensic data from the internal network or Cloud when performing a forensic investigation. This highlights the fact that Forensics can be divided into three categories: IoT device level, network forensics and cloud forensics. This is relevant because IoT forensics is heavily dependent on cloud forensics (as a lot of data is stored in the cloud) and analyzing the communication between devices in addition to data gathered from the physical device or sensor.

Why IoT Forensics is needed

The proliferation of Internet connected devices and sensors have made life a lot easier for users and has a lot of benefits associated with it. However, it also creates a larger attack surface which is vulnerable to cyberattacks. In the past IoT devices have been involved in incidents that include identity theft, data leakage, accessing and using Internet connected printers, commandeering of cloud-based CCTV units, SQL injections, phishing, ransomware and malware targeting specific appliances such as VoIP devices and smart vehicles.

With attackers targeting IoT devices and then using them to compromise enterprise systems, we need the ability to extract and review data from the IoT devices in a forensically sound way to find out how the device was compromised, what other systems were accessed from the device etc.

In addition, the forensic data from these devices can be used to reconstruct crime scenes and be used to prove or disprove hypothesis. For example, data from a IoT connected alarm can be used to determine where and when the alarm was disabled and a door was opened. If there is a suspect who wears a smartwatch then the data from the watch can be used to identify the person or infer what the person was doing at the time. In a recent arson case, the data from the suspects smartwatch was used to implicate him in arson. (Reardon, 2018)

The data from IoT devices can be crucial in identifying how a breach occurred and what should be done to mitigate the risk. This makes IoT forensics a critical part of the Digital Forensics program.

Current Forensic Challenges Within the IoT

The IoT forensics field has a lot of challenges that need to be addressed but unfortunately none of them have a simple solution. As shown in the research done by M. Harbawi and A. Varol (Harbawi, 2017) we can divide the challenges into six major groups. Identification, collection, preservation, analysis and correlation, attack attribution, and evidence presentation. We will cover the challenges each of these presents in the paper.

A. Evidence Identification

One of the most important steps in forensics examination is to identify where the evidence is stored and collect it. This is usually quite simple in the traditional Digital Forensics but in IoT forensics this can be a challenge as the data required could be stored in a multitude of places such as on the cloud, or in a proprietary local storage.

Another problem is that since IoT fundamentally means that the nodes were in real-time and autonomous interaction with each other, it is extremely difficult to reconstruct the crime scene and to identify the scope of the damage.

A report conducted by the International Data Corporation (IDC) states that the estimated growth of data generated by IoT devices between 2005 to 2020 is going to be more than 40,000 exabytes (Yakubu et al., 2016) making it very difficult for investigators to identify data that is relevant to the investigation while discarding the irrelevant data.

B. Evidence Acquisition

Once the evidence required for the case has been identified the investigative team still has to collect the information in a forensically sound manner that will allow them to perform analysis of the evidence and be able to present it in the court for prosecution.

Due to the lack of a common framework or forensic model for IoT investigations this can be a challenge. Since the method used to collect evidence can be challenged in court due to omissions in the way it was collected.

C. Evidence Preservation and Protection

After the data is collected it is essential that the chain of custody is maintained, and the integrity of the data needs to be validated and verifiable. In the case of IoT Forensics, evidence is collected from multiple remote servers, which makes maintaining proper Chain of Custody a lot more complicated. Another complication is that since these devices usually have a limited storage capacity and the system is continuously running there is a possibility of the evidence being overwritten. We can transfer the data to a local storage device but then ensuring the chain of custody is unbroken and verifiable becomes more difficult.

D. Evidence Analysis and Correlation

Due to the fact that IoT nodes are continuously operating, they produce an extremely high volume of data making it difficult to analyze and process all the data collected. Also, since in IoT Forensics there is less certainty about the source of data and who created or modified the data, it makes it difficult to extract information about ownership and modification history of the data in question.

With most of the IoT devices not storing metadata such as timestamps or location information along with issues created by different time zones and clock skew/drift it is difficult for investigators to create causal links from the data collected and perform analysis that is sound, not subject to interpretation bias and can be defended in court.

E. Attack and Deficit Attribution

IoT forensics requires a lot of additional work to ensure that the device physical and digital identity are in sync and the device was not being used by another person at the time. For example, if a command was given to Alexa by a user and that is evidence in the case against them then the examiner needs to confirm that the person giving the command was physically near the device at the time and that the command was not given over the phone remotely.

F. Evidence Presentation

Due to the highly complex nature of IoT forensics and how the evidence was collected it is difficult to present the data in court in an easy to understand way. This makes it easier for the defense to challenge the evidence and its interpretation by the prosecution.

VI. Opportunities of IoT Forensics

IoT devices bring new sources of information into play that can provide evidence that is hard to delete and most of the time collected without the suspect’s knowledge. This makes it hard for them to account for that evidence in their testimony and can be used to trip them up. This information is also harder to destroy because it is stored in the cloud.

New frameworks and tools such Zetta, Kaa and M2mLabs Mainspring are now becoming available in the market which make it easier to collect forensic information from IoT devices in a forensically sound way.

Another group is pushing for including blockchain based evidence chains into the digital and IoT forensics field to ensure that data collected can be stored in a forensically verifiable method that can’t be tampered with.

Conclusion

IoT Forensics is becoming a vital field of investigation and a major subcategory of digital forensics. With more and more devices getting connected to each other and increasing the attack surface of the target it is very important that these devices are secured and have a sound way of investigating if and when a breach happens.

Tools using Artificial Intelligence and Machine learning are being created that will allow us to leverage their capabilities to investigate breaches, attacks etc faster and more accurately.

References

Reardon. M. (2018, April 5). Your Alexa and Fitbit can testify against you in court. Retrieved from https://www.cnet.com/tech/mobile/alexa-fitbit-apple-watch-pacemaker-can-testify-against-you-in-court/.

M. Harbawi and A. Varol, “An improved digital evidence acquisition model for the Internet of Things forensic I: A theoretical framework”, Proc. 5th Int. Symp. Digit. Forensics Security (ISDFS), pp. 1-6, 2017.

Yakubu, O., Adjei, O., & Babu, N. (2016). A review of prospects and challenges of internet of things. International Journal of Computer Applications, 139(10), 33–39. https://doi.org/10.5120/ijca2016909390

Note: This was originally written as a paper for one of my classes at EC-Council University in Q4 2021, which is why the tone is a lot more formal than my regular posts.

– Suramya

January 28, 2022

IoT Devices and Reducing their Impact on Enterprise Security

Filed under: Article Releases,Computer Security,My Thoughts,Security Tutorials,Tech Related — Suramya @ 11:34 PM

IoT devices are becoming more and more prevalent in the corporate world, as they allow us to automate tasks and activities without manual intervention, which increases the risk to the organization by increasing the attack surface available to attackers. This is because IoT devices can act as entry points to the organization’s internal network. In order to reduce the security impact of these devices the attack channels and threats from the devices need to be mitigated. This can be done by implementing the suggestions in this paper

IoT or Internet of Things is a collection of devices that are connected to the internet and can be controlled over a network or provide data over the internet. It is one of the fastest growing markets, with enterprise IoT spending growing by 24% in 2021 from $128.9 billion. (IoT Analytics, 2021). This massive growth brings new challenges to the table as administrators need to secure IoT devices in their network to prevent them from being security threats to the network.

IoT devices allow us to manage, monitor and control devices and sensors remotely which in turn allows us to automate tasks and activities without manual intervention. But this capacity comes at an increased risk of vulnerability due to a massive increase of the attack surface available. They are becoming more and more prevalent in an enterprise setting, especially in the office automation and operational technology areas. This increases the risk to the organization by increasing the possibility of threats in areas that traditionally don’t pose cyber security risks.

IoT devices can act as entry points to an organizations internal network and be used to exfiltrate data from the network without raising flags. In 2018, attackers used a compromised IoT thermometer in the lobby aquarium of a casino to breach their system and exfiltrate their high-roller database (~10GB of data) out of the corporate network to servers they controlled via the thermostat. (Williams-Grut, 2018).

In this paper we will review some of the major threats and attack channels targeting IoT devices and look at how we can reduce the impact of these threats on the enterprise security.

IoT Threats and Attack Channels

IoT devices have multiple attack surfaces due to their design and usage. We will cover the major vulnerabilities in this section along with mitigation steps for each threat and attack channel.

A. Physical Vulnerabilities

Since these devices are usually physically deployed in the field in addition to the typical software and communication vulnerabilities, they are also vulnerable to physical attacks where the device can be physically modified to gain access. Some of the examples of Physical attacks are as follows:

  • Attackers physically remove the device memory or flash chips to read & analyze the data and software on the chip.
  • Attackers tamper with the microcontroller to gain access to or identify sensitive information
  • Physically modify the device to return incorrect data or telemetry. For example, camera’s or motion sensors overseeing sensitive locations could be modified to ignore breaches.
  • Use the device connectivity to act as a bridge to gain access to the corporate network.
  • Attackers authenticate locally to the device using debug interface on the device to gain access to the device internals

The best way to protect against such attacks is to ensure the following preventive measures are taken for all devices on the network:

  • Ensure that the device or sensor is not easily accessible physically.
  • All sensors and devices should have tamper proof seals installed on them with regular checks to verify that they are not tampered with.
  • Unused ports, connections, diagnostic connectors etc should be physically disabled when possible.
  • If possible, ensure the devices have hardware-based security checks on it.

B. Outdated Firmware

Many of the IoT devices and sensors run older versions of Linux with no easy way to update the firmware, installed software or applications to the latest versions. This creates a major security risk as the device is running software with known security vulnerabilities which allows attackers to easily compromise a device.

There is no easy way to resolve this problem and protect the devices as a lot of these sensors and devices are not designed with security in mind. The best way to approach this problem is to ensure you are working with reputable device manufacturers who will ensure that appropriate support and updates are going to be available for the device/sensor.

The organization should review the recommendations by the IoT working group of the Cloud Security alliance on how to perform IoT Firmware updates securely and regularly. (Khemissa et al., 2018) The should also include the IoT sensors and devices in the organization’s update cycles which will allow them to ensure that patches and updates are installed in a timely manner on them.

Another option is to explore installing open source firmware and software on the IoT device/sensor if this option is available. The opensource firmware’s are usually updated more frequently and can be customized to better secure the device.

C. Hard Coded Passwords/Accounts

Some of the IoT devices have hard coded account passwords that cannot be changed, and this gives an attacker backdoor access to the device that is difficult to protect against. Hardcoded passwords are particularly dangerous because they are easy targets for password guessing exploits, allowing attackers to hijack firmware, devices, systems, and software etc. A famous case of such an exploit was found in 2017 when researchers found default hardcoded passwords in IoT camera’s manufactured by Foscam. (Heller, 2017) that gave admin access to anyone who used them. These passwords allow an attacker to gain access to the device and use it as a launch surface against attacks on the network.

Another famous attack exploiting this was by the Mirai malware in 2016. It scanned for and exploited Linux-based IoT boxes with Busybox (such as DVRs and WebIP Cameras) using hardcoded usernames and passwords. Once it gained access these devices were enrolled in a botnet containing over 400,000 connected devices which were then used to perform DDoS attacks on major companies across the world. (Fruhlinger, 2018)

To protect against these attacks, we should ensure the default passwords on all devices are changed frequently. An active pentest against the device should be conducted to uncover any hidden or hardcoded accounts. If any are found, the manufacturer should be contacted to prove an update to disable these accounts.

D. Poor IoT device management

A study published in July 2020 found that almost 15% of IoT devices on an enterprise network were unknown or unauthorized and between 5 to 19% of these devices were using unsupported legacy operating systems (Help Net Security, 2020). These devices make up what is known as a Shadow IoT network that is implemented without the knowledge of the organization’s IT team and can be a major weak point in the organization’s security perimeter.

The best way to protect against this scenario is to ensure regular scans are done on the network to identify any unknown or new devices connected to the network. The pentest will enable us to identify these unauthorized devices which can then be incorporated into the official network and update cycle or disconnected depending the requirements. Another way to find these unauthorized devices is to monitor and analyze network connections and traffic. New devices will change the network data flow, and this can be used to identify or locate new devices or sensors connected to the network.

E. Man-in-the-Middle Attacks

Communication channels in IoT devices are usually very trivially protected and an attacker can compromise the channel to intercept the messages between devices and modify them. This allows the attacker to cause malfunctions or show incorrect data. This can potentially cause serious harm if the targeted IoT devices are connected to or managing industrial or medical equipment. It can also allow attackers to hide their tracks and physical evidence of their work.

F. Industrial Espionage & Eavesdropping

IoT devices such as cameras, microphones etc are used to monitor sensitive areas or devices for problems remotely. If an attacker compromises these cameras, they allow them to visually and audially monitor their target compromising their privacy and potentially gaining access to sensitive data or video. For example, IoT cameras deployed in bedrooms have been used to record and leak intimate videos of the residents without their knowledge. Compromised security cameras have been used to record ATM pins entered by unsuspecting users.

Other steps that should be taken to reduce risk from IoT devices on your network:

  • Segregate your Networks: IoT devices should be on a separate segment of the network which is isolated from the production and user network with a firewall sitting between the two. This will allow you to block access to the production network from the IoT network which will prevent an attacker from gaining full access to the enterprise network in case they breach the IoT network.
  • Enable HTTPS/Encrypted connectivity for IoT devices: All connections to and from the IoT devices should be encrypted to protect against Man-in-the-middle attacks.
  • Deploy an IDS: Deploying an Intrusion Detection System (IDS) on the network can alert us to attack attempts. All alerts from the IDS should be investigated and verified.

These are just some of the attack surfaces available to attackers targeting IoT devices, in fact with the increase in computing power available to these devices they are almost mini computers and most of the attacks that impact traditional systems such as servers or desktops can target IoT devices as well with minimal modifications. So, it is essential that security trainings are conducted for all employees in the organization to make them aware of the risks posed by IoT devices and train the security team in methods to secure these devices from attackers.

Note: This was originally written as a paper for one of my classes at EC-Council University in Q3 2021, which is why the tone is a lot more formal than my regular posts.

– Suramya

January 20, 2022

Impact of Google Hacking and Data Collection using Search Engines on CyberSecurity

Filed under: Article Releases,Computer Security,My Thoughts,Tech Related — Suramya @ 1:58 AM

The modern search engines scan most of the public sites on a regular basis and unlike the legacy search engines also have the capability of finding and indexing data or files that are not linked to from any other sources. This allows the search engine to index data/files that could have sensitive data or details on vulnerabilities. Using publicly available information attackers can perform searches for such information without touching the target system directly leaving little trace for the defenders to watch for to be alerted. Most organizations are not aware of the information being leaked by such means and how it is compromising their cyber security. The availability of the Google Hacking Database allows even minimally skilled attackers to search for information quickly and efficiently.
This poses a high risk to the organizations leaking sensitive data. There are no sure shot solutions to this problem and even the most careful organizations will expose data that when combined with other sources allow attackers a look at the organizations digital assets and systems.

The popular image of a hacker involves an attacker sitting in a dark room typing commands in a terminal to gain access and usually is completed in a very short period of time. In real life attackers spend a lot of time performing reconnaissance on the target before even engaging with the target system. One of the popular ways of performing reconnaissance is to use search engines like Google to find data, this technique is called Google Hacking and was introduced to public in 2004 by Johnny Long. He defined it as “the art of creating complex search engine queries in order to filter through large amounts of search results for information related to computer security” (Johnny, 2004). Attackers use Google Hacking to uncover sensitive information about a company or uncover potential security vulnerabilities.

The modern search engines scan most of the public sites on a regular basis and unlike the legacy search engines also have the capability of finding and indexing data or files that are not linked to from any other sources. This allows the search engine to index data/files that could have sensitive data or details on vulnerabilities.

The Google Hacking Database (GHDB) is a consolidated database of queries that have been collected over the years thanks to contributions by researchers, hackers and general public that can be used to find sensitive data on websites such as files containing passwords, configurations, sensitive data, financial information, error messages, firewall logs and other such data. (Google Hacking Database, 2021) The database is in an easy to consume format and allows users to search for queries that will return specific types of data.

This database gives attackers the queries to be used to specific types of data, leveraging the indexing powers of Google for finding information that should not have been exposed to the public.

How Google Hacking Works

Google allows a user to search for information using search keywords and a combination of search operators to limit the search results. With the information available in the Google Hacking Database an attacker can search for specific information and limit the search to a given target domain. There are multiple kinds of queries available that target specific kinds of information. Some of the categories of information available using this are:

  • Advisories and vulnerabilities: Queries that allow us to locate vulnerable servers based on product or version-specific setups with known vulnerabilities..
  • Sensitive directories: Allow us to find directories with files that contain sensitive information
  • Files containing passwords: Locate files containing passwords.
  • Pages containing login portals: Locate login pages for various services
  • Error messages: Find files with errors messages that may contain details about the system.

Below are examples of the various queries that are available and the kind of data they expose.

Searching for passwords stored in files

Users sometimes store passwords in plain text files or excel databases that are accidentally uploaded to a public site. These are then indexed by Google (or other search engines) and can be found using specific queries. For example:

allintext:"*.@gmail.com" OR "password" OR "username" filetype:xlsx

searches for all Excel files that have gmail.com in the text along with “password”. This will find all files containing any of the search terms provided. If required we can limit the search to a specific site using the “site:” search parameter.

Search for Log files

Log files contain a lot of sensitive information if exposed to public. Error logs, access logs can expose information such as PHP version you are running, CMS version details, Operating system details etc. If firewall logs or system logs are exposed it can reveal information such as usernames, firewall version and configuration details etc. Similarly SQL logs can expose sensitive data as well. This information combined with other information can give an attacker a foothold in the system. For example:

allintext:username filetype:log

This query will give results that include the text username inside all *.log files and the following query will return all directories where logfiles are publicly accessible:

intitle:"index of" errors.log

SSH private keys

SSH private keys are used to encrypt/decrypt data exchanged during SSH connections. They also allow users to authenticate to servers without the use of passwords. If they are exposed anyone can impersonate that user and if passwordless login’s are enabled the key will allow the attacker to login to the server without a password. The following query will return all directories with publicly accessible private key:

intitle:index.of id_rsa -id_rsa.pub

Login Portals

A lot of times organizations expose their development or staging systems to the internet for testing and depend on the obscurity of the system for protection. These systems are vulnerable because development systems often don’t have the same protections and controls applied on them as production systems do. In addition, there are often systems that were not meant to be pubic such as router login pages, CMS admin sections etc that increase the attack surface of the organization. A sample query to find login pages for CISCO email security appliance is listed below:

intitle:"Cisco Email Security Virtual Appliance" inurl:csrfkey=

SQL dumps

Sometimes sites require SQL datadumps to be made for backup or restoration purposes and these dumps often have a lot of sensitive data in them. Using a search query similar to the one listed below attackers can find these dumps and explore the data:

ext:sql | ext:txt intext:"-- phpMyAdmin SQL Dump --" + intext:"admin"

There are many more queries that are available in the database to search for specific data and more are added everyday.

Famous attacks that used Google Hacking/Google Dorks
Attacks using Google Hacking/Google Dorks are difficult to identify due to the passive nature of the attacks. However, even with that restriction there have been a few cases of note where the attacker’s used this technique to attack an organization’s system and some of them are listed below.

N.Y. Dam attack from Iran, 2013

Between 2011 and 2013, Hamid Firoozi from Iran gained access to the Bowman Avenue Dam in Rye Brook, New York by finding an unprotected computer that controlled the dam’s sluice gates using Google Searches. (Matthews, 2016). The issue is rampant enough that the Department of Homeland Security and FBI jointly released a warning about Google dorking. “By searching for specific file types and keywords, malicious cyber actors can locate information such as usernames and passwords, e-mail lists, sensitive documents, bank account details, and website vulnerabilities,” (FBI, 2014)

Detection of Google Hacking Attacks

Detection of these attacks is difficult due to the passive nature of the attack. However, one of the technique that is quite successful is to use a Honey Pot approach. Organizations can store files with fake information that looks authentic and important such as username and password combinations or SSH private keys that belong to non-existent accounts. Because these accounts do not exist no one should be attempting to log in to them for legitimate purposes so when a login attempt is made to these accounts or when the files are accessed we know that a Google Hacking attack is in progress and the IP address etc can be flagged for followup or blocking. We can also lure the system into a fake network which is monitored to identify what information they are looking for in the network.

Using that information, we can take further preventive measures to protect the system.

Prevention Techniques for Google Hacking attacks

There are a few steps that we can take to avoid leaking sensitive data to attackers using Google Dorks as listed below:

  • Protect sensitive data with authentication for private information
  • Don’t expose development systems to internet, if that is not possible restrict access using IP based restriction.
  • Run regular vulnerability scans on your website/domain. A lot of the scanners now incorporate checks for popular Google Dork queries
  • Run manual dork queries against your site to locate leaks before attackers do
  • Add checks to your servers to find sensitive files in public directories such as any file with an extension other than a php/asp/html. These can we potential leaks
  • If you find sensitive content exposed, you can request its removal by using the Google Search Console.

Conclusion

Google Hacking allows an attacker to perform reconnaissance against your organization in a passive way allowing them to collect information that can then be combined with other sources to give them a foot hold. Preventing such information leaks is a good way to protect the organizational systems and the techniques listed above can help with that. We can also subscribe to services that perform these checks on your behalf.

We covered some of the techniques available to detect and prevent Google Hacking attacks in the paper and while the techniques discussed will not protect against all attacks, they will reduce the attack surface and protect you against most attackers.

Note: This was originally written as a paper for one of my classes at EC-Council University in Q2 2021, which is why the tone is a lot more formal than my regular posts.

March 4, 2006

Released Ver 2 of DFS Install Guide

Filed under: Article Releases,Linux/Unix Related,Website Updates — Suramya @ 5:10 PM

Hi Everyone,
I have released Ver 2 of my DFS (Debian From Scratch) install guide. This version covers the installation of dfs-0.6.19

Article URL: How to install and configure DFS.

Thanks,
Suramya

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