Suramya's Blog : Welcome to my crazy life…

April 24, 2022

Smart-contract bug locks away $34 million highlighting major weakness in smart-contracts

Filed under: Computer Software,Emerging Tech,Tech Related — Suramya @ 9:57 PM

Over the years I have had many conversations with people about Blockchain and how it is supposed to solve all our problems, but for the most part I think Blockchain is overrated and doesn’t solve any problem that can’t be solved in an easier way using less resources. Then as if Blockchain’s were not enough someone went and created smart contracts which are basically programs stored on a blockchain that run when predetermined conditions are met. They typically are used to automate the execution of an agreement so that all participants can be immediately certain of the outcome, without any intermediary’s involvement or time loss. They can also automate a workflow, triggering the next action when conditions are met. – IBM Smart-Contracts Def

The major issue with a blockchain contract is that the contract is immutable so if there is a bug in the program no one can modify it to fix the issue. When warned about this potential problem the proponents of the smart-contract pretty much handwaved the concerns away stating that the issue is not that big a deal and people were just opposing them because they dislike smart-contracts and are sticks in mud etc etc.

Unfortunately, this is no longer a theoretical issue as the developers of the AkuDreams contract found out over the weekend. Due to a bug in the contract code $34 million, or 11,539 eth, is permanently locked into the AkuDreams contract forever. It cannot be retrieved by individual users or by the dev team..

This shows how dangerous it is to have a program that can’t be modified because no matter what we do we can’t ensure that code written will be 100% bug free in all the cases. When there is a bug in regular software be can push out a patch to fix it, but that is not an option for smart-contracts and that as you can see becomes an expensive issue.

Source: $34M permanently locked into AkuDreams contract forever due to bad code

– Suramya

April 23, 2022

Molecular engines made of protein could power molecular machines

Filed under: Emerging Tech,My Thoughts — Suramya @ 11:48 PM

Nano-machines have long been staples of Sci-Fi stories where nanotech is used to cure illnesses , make new materials, kill people etc etc and in the recent years a lot of effort has been put in to make these machines real. Basically speaking, a nanomachine, also called a nanite, is a mechanical or electromechanical device whose dimensions are measured in nanometers (millionths of a millimeter, or units of 10 -9 meter). [What is Nanite] They are still in the R&D phase but a lot of progress is being made in the field.

Researchers at the University of Washington in Seattle have created the first building blocks of a molecular engine, namely the axles and rotors. The really cool part is that these are self assembling and use custom designed new proteins unlike any found in nature. The researchers used the advances in Deep learning software to predict what shape a given DNA sequence will fold into making it easier to find a code that makes the desired shape. This allows them to create custom shapes without having to modify existing molecules which can be quite hard.

The team made the machine parts by putting DNA coding for the custom proteins into E. coli bacteria, and then checked their structure using a method called cryogenic electron microscopy.

This showed that the axles assembled correctly inside the rotors, and also revealed the different configurations that would be expected if the axles were turning. But because cryogenic electron microscopy can only provide a series of stills rather than a moving picture, the team can’t say for sure if the axles are rotating.

If they are, it would only be a random back-and-forth movement driven by molecules knocking into each other, a phenomenon called Brownian motion. The team is now designing more components to drive the motion in one direction and create a rotary engine, says Baker.

The work is still in the preliminary stage and the team is designing more components to drive the motion in one direction and create a rotary engine to make sure that the movement seen in the current trials is not just due to Brownian motion. Once the technology is perfected it has a lot of use cases in fields such as BioMed to remove tumors, clean out arterial blocks, repair injuries and fields like material design where these machines can be used to create new materials which are stronger and lighter.

Source: Tiny axles and rotors made of protein could drive molecular machines
Paper: Computational design of mechanically coupled axle-rotor protein assemblies

– Suramya

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 21, 2022

It is possible to plant Undetectable Backdoors in Machine Learning Models

Filed under: Computer Security,Computer Software,Emerging Tech,My Thoughts,Tech Related — Suramya @ 2:23 AM

Machine learning (ML) is the big thing and ML algorithms are slowly creeping into all aspects of our life such as unlocking your phone using facial recognition, evaluating the eligibility for a loan, surveillance, what ads you see when surfing the web, what search results you get when searching for stuff etc etc. The problem is that ML algorithms are not infallible they depend on the training data used, confirmational bias etc. At the very least they enforce the existing bias for example, if a company only hires men 25-45 for a role then the ML data set will take this as the input and all future candidates will be evaluated against this criteria because the system thinks that this is what a success looks like. The algorithms themselves are getting more and more complicated and it is almost impossible to review and validate the findings. Due to this decisions are being made by machines that can’t be audited easily. Plus it doesn’t help that most ML models are proprietary and the companies refuse to let outsiders examine them due to Trade secrets and proprietary information used in them.

Another problem is that these ML models is adversarial perturbations where attackers make minor changes to the image/data going in to get a specific response/output. There are a lot of examples of this in the past few years and some of them are listed below (Thanks to Cory Doctorow for consolidating them in one place)

These all take advantage of flaws in the ML model that can be exploited using minor changes in the input data. However, there is another major exploit surface available which is incredibly hard to protect against: Backdoors in the ML models by creating a model that will accept a particular entry/key to produce a specific output. The ‘best’ part is that it is almost impossible to detect if this has been done because the model will function exactly the same as an un-tampered model and will only show the abnormal behavior for the specific key which would have been randomly generated by the creator during the training. If done well then the modifications will be undetectable for most tests.

A team for MIT and IAS has written a paper on it (“Planting Undetectable Backdoors in Machine Learning Models“) where they go into details of how this can be done and the potential impact. Unfortunately, they have not been able to come up with a feasible defense against this attack as of this time. Hopefully that will change as others start focusing on this problem and how to solve it.

Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate “backdoor key”, the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.

First, we show how to plant a backdoor in any model, using digital signature schemes. The construction guarantees that given black-box access to the original model and the backdoored version, it is computationally infeasible to find even a single input where they differ. This property implies that the backdoored model has generalization error comparable with the original model. Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm or in Random ReLU networks. In this construction, undetectability holds against powerful white-box distinguishers: given a complete description of the network and the training data, no efficient distinguisher can guess whether the model is “clean” or contains a backdoor.

Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, our construction can produce a classifier that is indistinguishable from an “adversarially robust” classifier, but where every input has an adversarial example! In summary, the existence of undetectable backdoors represent a significant theoretical roadblock to certifying adversarial robustness.

The paper is still waiting for the peer-review to complete but the concept and methods they describe seem solid so this is a problem we will have to solve sooner rather than later considering the speed with which ML models are impacting our life.

Source: Schneier on Security: Undetectable Backdoors in Machine-Learning Models

– 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

August 7, 2021

Bypass of Facial Recognition made possible by creating Master faces that impersonate 40% of population

Filed under: Computer Security,Emerging Tech,My Thoughts,Tech Related — Suramya @ 9:00 PM

Over the years, there has been a lot of push for Image recognition systems and more and more companies are entering the field each with their own claims of supernatural accuracy. Plus, with all the amazing ‘tech’ being showcased in the movies and on TV people are primed to expect that level of accuracy. Unfortunately, reality is a lot more weird and based on research its pretty simple to fool image recognition systems. In the past people have tricked systems to misidentifying a banana as a toaster by modifying parts of the image. There was another recent event where the Tesla self navigation system kept thinking the moon was a Yellow light and insisted on slowing down. There are so many of these ‘edge’ cases that it is not even funny.

A specific use case for image recognition is Facial recognition and that is a similar mess. I have personally used a photo of an authorized user to get a recognition system to unlock a door during testing. We have cases where wearing glasses confuses the system that it locks you out. Now according to research conducted by the Blavatnik School of Computer Science and the school of Electrical Engineering it is possible to create a ‘master’ face that can be used to impersonate multiple ID’s. In their study they found that the 9 faces created by the StyleGAN Generative Adversarial Network (GAN) could impersonate 40% of the population. Testing against the University of Massachusetts’ Labeled Faces in the Wild (LFW) open source database they were able to impersonate 20% of the identities in the database with a single photo.

Basically, they are exploiting the fact that most facial recognition systems use broad sets of markers to identify specific individuals and StyleGAN creates a template containing multiple such markers which can then be used to fool the recognition systems.

Abstract: A master face is a face image that passes face-based identity-authentication for a large portion of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user-information. We optimize these faces, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. Multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network in order to direct the search in the direction of promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a high coverage of the population (over 40%) with less than 10 master faces, for three leading deep face recognition systems.

Their paper has been published and is available for download here: Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution.

With more and more companies pushing for AI based recognition systems as fool proof systems (looking at you Apple, with your latest nonsense about protecting kids by scanning personal photos) it is imperative that more such research is conducted before these systems are pushed into production based on the claims in their marketing brochures.

Thanks to Schneier on Security: Using “Master Faces” to Bypass Face-Recognition Authenticating Systems

– Suramya

June 16, 2021

New material created that shows zero heat expansion from 4 to 1,400 K

Filed under: Emerging Tech,My Thoughts — Suramya @ 11:53 PM

One of the issues with high performance systems is that they generate a lot of great and heat usually causes the material they are made of to expand. Similarly cold temperatures causes materials to contract and this can cause problems because the constant expansion and contraction weakens the material. Due to this there is a lot of research that is happening to find materials that don’t expand/contract so much with temperature changes.

Researches from Australia have created a material that has zero thermal expansion. The material made out of scandium, aluminum, tungsten and oxygen did not expand or contract even when subjected to changes from 4 to 1,400 Kelvin (-269 to 1126 °C, -452 to 2059 °F). This makes it could make orthorhombic Sc1.5Al0.5W3O12 very useful for devices that need to work in extreme temperatures. This is a phenomenal achievement with a ton of uses. However the components to make the material are not cheap especially Scandium which is one of the most expensive elements currently. According to folks online it can cost about ~$120/gram so unless other elements can be used or we find a easy to mine/extract source of the metal the material is not something that we will see in general use anytime soon.

Zero thermal expansion (ZTE) is a rare physical property; however, if accessible, these ZTE or near ZTE materials can be widely applied in electronic devices and aerospace engineering in addition to being of significant fundamental interest. ZTE materials illustrate this property over a certain temperature range. Here, orthorhombic (Pnca space group) Sc1.5Al0.5W3O12 is demonstrated to deliver ZTE over the widest temperature reported to date, from 4 to 1400 K, with a coefficient of thermal expansion of αv = −6(14) × 10–8 K–1. Sc1.5Al0.5W3O12 maybe is one of the most thermally stable materials known based on the temperature range of stability and the consistent thermal expansion coefficients observed along the crystallographic axes and volumetrically. Furthermore, this work demonstrates the atomic perturbations that lead to ZTE and how varying the Sc:Al ratio can alter the coefficient of thermal expansion.

This material has a ton of uses. For example, this would be very useful in making items or structures in space. Since the temperature in space can vary from ~260 degrees Celsius in the sunlight to below -100 degrees Celsius in the shade we need materials with a low expansion coefficient. Another use case is for a coating on hypersonic jets, recently China has created a Mach 30 wind tunnel which allows them to test prototypes for planes that can fly at Mac 30. At that speed the air turns into plasma due to the friction and requires the planes to be made (or atleast coated with) a material that has low/zero heat expansion. If these planes are coated with this material then the only limitation on the speed would be how much thrust the engines can provide.

I can also see it being used for military jets/missiles etc to allow them to fly faster without damage and on rockets to make them more durable with lesser weight disadvantages.

The paper was published in Chemistry of Materials journal and though the work has a long way to go before it is commercially available it does have some fascinating potential.

Source: Extraordinary new material shows zero heat expansion from 4 to 1,400 K

– Suramya

June 14, 2021

New technique Lets Users Preview Files Stored in DNA Data Storage

Filed under: Computer Hardware,Emerging Tech,Science Related,Tech Related — Suramya @ 7:45 AM

Using DNA for storage is an idea that has been around for a while with the initial idea of DNA storage being postulated by Richard P. Feynman in 1959. It was mostly a theoretical exercise till 1988, when researchers from Harvard and the artist Joe Davis stored an image of an ancient Germanic rune representing life and the female Earth in the DNA sequence of E.coli. After that In November 2016 (Lot more stuff happened between the two dates and you can read it all on the Wiki page), a company called Catalog encoded 144 words from Robert Frost’s famous poem, “The Road Not Taken” into strands of DNA. Pretty soon after that in June 2019, scientists reported that all 16 GB of text from Wikipedia’s English-language version have been encoded into synthetic DNA.

DNA storage has been becoming easier and cheaper as time goes on with more and more companies getting on the bandwagon. Even Microsoft has a DNA Storage Research project. However, even with all the advances so far there is a lot more work required before this becomes stable, cheap and reliable enough to be a commercial product. One of the problems that we faced with the storage in the past was that it wasn’t possible to preview the data stored in DNA. You had to open the entire file if you wanted to know what was in it. Think of trying to browse an image gallery without thumbnails, you would have to open each file to see what it was when trying to find a particular file.

Researchers from North Carolina State University have developed a way to provide previews of a stored data file similar to how a thumbnail works for image files. Basically they used the fact that when files have similar file names then the system will copy pieces of multiple data files. Till now this was a problem but the researchers figured out how to use this behavior to allow them to either open the entire file or a subset.

“The advantage to our technique is that it is more efficient in terms of time and money,” says Kyle Tomek, lead author of a paper on the work and a Ph.D. student at NC State. “If you are not sure which file has the data you want, you don’t have to sequence all of the DNA in all of the potential files. Instead, you can sequence much smaller portions of the DNA files to serve as previews.”

Here’s a quick overview of how this works.

Users “name” their data files by attaching sequences of DNA called primer-binding sequences to the ends of DNA strands that are storing information. To identify and extract a given file, most systems use polymerase chain reaction (PCR). Specifically, they use a small DNA primer that matches the corresponding primer-binding sequence to identify the DNA strands containing the file you want. The system then uses PCR to make lots of copies of the relevant DNA strands, then sequences the entire sample. Because the process makes numerous copies of the targeted DNA strands, the signal of the targeted strands is stronger than the rest of the sample, making it possible to identify the targeted DNA sequence and read the file.

However, one challenge that DNA data storage researchers have grappled with is that if two or more files have similar file names, the PCR will inadvertently copy pieces of multiple data files. As a result, users have to give files very distinct names to avoid getting messy data.

“At some point it occurred to us that we might be able to use these non-specific interactions as a tool, rather than viewing it as a problem,” says Albert Keung, co-corresponding author of a paper on the work and an assistant professor of chemical and biomolecular engineering at NC State.

Specifically, the researchers developed a technique that makes use of similar file names to let them open either an entire file or a specific subset of that file. This works by using a specific naming convention when naming a file and a given subset of the file. They can choose whether to open the entire file, or just the “preview” version, by manipulating several parameters of the PCR process: the temperature, the concentration of DNA in the sample, and the types and concentrations of reagents in the sample.

The new technique is compatible with the DNA Enrichment and Nested Separation (DENSe) system that enables us to make DNA storage systems more scalable. The researchers are looking for industry partners to explore commercial viability. If things work out then maybe in the near future we could start storing data in biological samples (like spit). Although, it does sound gross to be handling spit and other bio matter when searching for saved data.

Source: New Twist on DNA Data Storage Lets Users Preview Stored Files
Paper: Nature.com: Promiscuous molecules for smarter file operations in DNA-based data storage

– Suramya

June 10, 2021

Using Graphene layers to store 10 times more data in Hard Disks

Filed under: Computer Hardware,Emerging Tech,Tech Related — Suramya @ 5:39 PM

The requirement for data storage has been going up exponentially over the past few years. At the start of 2020 it was estimated that the amount of data in the world was approximately 44 zettabytes (44,000,000,000,000,000,000,000 bytes), by 2025 this number will have grown to 175 zettabytes of data (Source). This means that we need better storage media to store all the information being generated. Imagine having to store this much data on floppy disks with their 1.4MB of storage or the early hard-disks that stored 10MB of data.

New research carried out in collaboration with teams at the University of Exeter, India, Switzerland, Singapore, and the US have replaced the carbon-based overcoats (COCs) which are basically layers on top of hard disk platters to protect them from mechanical damage with 2-4 layers of Graphene. Since we have reduced the thickness of the COC layer the platters can be placed closer together allowing us to have a greater storage density per inch and basically multiply the storage capacity by a factor of ten. Another advantage of using Graphene is that it reduces the corrosion of the platters by 2.5 times thereby making drives more reliable and increasing their lives.

HDDs contain two major components: platters and a head. Data are written on the platters using a magnetic head, which moves rapidly above them as they spin. The space between head and platter is continually decreasing to enable higher densities. Currently, carbon-based overcoats (COCs) — layers used to protect platters from mechanical damages and corrosion — occupy a significant part of this spacing. The data density of HDDs has quadrupled since 1990, and the COC thickness has reduced from 12.5nm to around 3nm, which corresponds to one terabyte per square inch. Now, graphene has enabled researchers to multiply this by ten.

The Cambridge researchers have replaced commercial COCs with one to four layers of graphene, and tested friction, wear, corrosion, thermal stability, and lubricant compatibility. Beyond its unbeatable thinness, graphene fulfills all the ideal properties of an HDD overcoat in terms of corrosion protection, low friction, wear resistance, hardness, lubricant compatibility, and surface smoothness. Graphene enables two-fold reduction in friction and provides better corrosion and wear than state-of-the-art solutions. In fact, one single graphene layer reduces corrosion by 2.5 times. Cambridge scientists transferred graphene onto hard disks made of iron-platinum as the magnetic recording layer, and tested Heat-Assisted Magnetic Recording (HAMR) — a new technology that enables an increase in storage density by heating the recording layer to high temperatures. Current COCs do not perform at these high temperatures, but graphene does. Thus, graphene, coupled with HAMR, can outperform current HDDs, providing an unprecedented data density, higher than 10 terabytes per square inch.

The research was published in Nature: Graphene overcoats for ultra-high storage density magnetic media and has a lot of promise but is still in research phase so it might be a little while before we see consumer products with Graphene layers. A more userfriendly / less technical overview is available at: Phys.org: Ultra-high-density hard drives made with graphene store ten times more data

– Suramya

May 23, 2021

Rapid Prototyping by Printing circuits using an Inkjet Printer

Filed under: Computer Hardware,Emerging Tech,Tech Related — Suramya @ 10:50 PM

Printing circuits using commercial inkject printers is something that is becoming more and more convenient and affordable day by day. In their 2014 paper Instant inkjet circuits: lab-based inkjet printing to support rapid prototyping of UbiComp devices Prof. Kawahara and others showcased several applications from touch sensors to capacitive liquid level sensors. If you are interested in trying this out (I am sorely tempted), then checkout this Instructable.com: Print Conductive Circuits With an Inkjet Printer post that walks you through how to modify your printer.

The Ink to print these circuits is available for purchase online at novacentrix.com. You need the following to start printing circuits:

  • A low-cost printer such as EPSON WF 2010
  • Printing substrates like PET and glossy paper
  • Oven or hot plate for sintering & drying the ink
  • Empty refillable cartridges

A good area for experimentation would be for wearable circuits on clothing and other such places. But there are a ton of other applications especially in the embedded electronics market.

Well this is all for now. Will write more later.

Thanks to Hackernews: Rapid Prototyping with a $100 Inkjet Printer for the link.

– Suramya

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