Exploring Deep Learning for Hardware Attacks

The international community firmly recognizes cyber-attacks as a serious fear that could endanger the global economy. The Global Risks 2015 report, published by the World Economic Forum, included this rather strong warning: “90 percent of companies worldwide recognize that they are insufficiently prepared to protect themselves against cyber-attacks”. Even worse, the Center for Strategic and International Studies estimates that cyber attacks and cybercrime already cost the global economy over US$400 billion per year! Attacks are likely to increase in size and to diversify in nature, driven by the expanding number of services available online and the increasing sophistication of cyber criminals who are engaged in a cat-and-mouse game with security experts. The impact of attacks can even be catastrophic when it comes to critical infrastructures such as power grids and nuclear plants. Hence, there is an urgent need for robust solutions. Developing appropriate solutions requires a deep understanding of potential and/or existing attacks; these can have either a hardware or a software nature. As the state-of-the-art provides some good solutions for software attacks, hackers seem to move recently more to the exploration of hardware as a powerful mean of attacks. Examples of such attacks are fault injection, side channel analysis, and hardware Trojans. Among these, side channel attacks are recognized as powerful attacks as they do not require any insertion to the system (i.e., they are purely based on observations), making them undetectable. Side channel attacks come in many flavors; one of the most popular is the template-based power attack. In this case, the adversary uses an identical copy of the targeted device and performs a physical characterization to extract a profile of the device which is used later during the attack. As this step is very complex, hackers are trying to use deep learning for the physical characterization. Providing a solution against such attacks needs a deep and full understanding of such attack. This thesis explores the usage of deep learning as a way to advance the accuracy of the side channel attack. Two scenarios are developed and experimented with: one based on supervised learning and one on unsupervised learning. In the supervised scenario, the attacks are enhanced using data pre-processing, which improves the classification accuracy of the associated physical characterization method based on Convolutional Neural Network (CNN). In the unsupervised scenario, the attacks are trying to skip part of the physical characterization (called profiling) and apply clustering using Stacked Autoencoder neural network in order to retrieve potential secret information. The two scenarios are implemented and validated against the two of the most widely used cryptographic algorithms known as Advance encryption standard (AES) and Elliptic Curve Cryptography (ECC). The results show the usage of data pre-processing techniques increases the accuracy of attacks by 30% and 15% on AES software and hardware implementations, respectively. The results of the clustering model show that it is feasible to advance the power attacks by skipping the characterization/profiling phase. The results show a 60% prediction accuracy for both aligned and misaligned power traces of the ECC implementations.