Countermeasures against Fault Injection Attacks in Neural Networks and Processors

Machine learning has gained a lot of recognition recently and is now being used in many important applications. However, this recognition was limited in the hardware security area. Especially, very few approaches depend on this powerful tool to detect attacks during operation. This thesis reduces this gap in the field of fault injection attack...

A Survey on Machine Learning in Hardware Security

Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in other domains. This survey, as one of the early attempts, presents the usage of machine...

Instruction Flow-based Detectors against Fault Injection Attacks

Fault injection attacks are a threat to all digital systems, especially to the ones conducting security sensitive operations. Recently, the strategy of observing the instruction flow to detect attacks has gained popularity. In this paper, we provide a comparative study between three hardware-based techniques (i.e., recurrent neural network (RNN)...

Exploiting PUF Variation to Detect Fault Injection Attacks

The massive deployment of Internet of Things (IoT) devices makes them vulnerable against physical tampering attacks, such as fault injection. These kind of hardware attacks are very popular as they typically do not require complex equipment or high expertise. Hence, it is important that IoT devices are protected against them. In this work, we...

Smart Redundancy Schemes for ANNs against Fault Attacks

Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks....

Using Hopfield Networks to Correct Instruction Faults

Fault injection attacks pose an important threat to security-sensitive applications, such as secure communication and storage. By injecting faults into instructions, an attacker can cause information leakage or denial-of-service. Hence, it is important to secure the sensitive parts not only by detecting faults in the executed instructions but...

Deterministic and Statistical Strategies to Protect ANNs against Fault Injection Attacks

Artificial neural networks are currently used for many tasks, including safety critical ones such as automated driving. Hence, it is very important to protect them against faults and fault attacks. In this work, we propose two fault injection attack detection mechanisms: one based on using output labels for a reference input, and the other on...

Protecting IoT Devices through a Hardware-driven Memory Verification

Internet of things (IoT) devices are appearing in all aspects of our digital life. As such, they have become prime targets for attackers and hackers. An adequate protection against attacks is only possible when the confidentiality and integrity of the data and applications of these devices are secured. State-of-the-art solutions mostly address...

RNN-based Detection of Fault Attacks on RSA

Physical fault injection attacks are becoming an important threat to computer systems, as fault injection equipment becomes more and more accessible. In this work, we propose a new strategy to detect fault attacks in cryptosystems. We use a recurrent neural network (RNN) to detect problems in the program flow caused by injected faults. Our...