Defense against On-Chip Trojans Enabling Traffic Analysis Attacks based on Machine Learning and Data Augmentation

Modern computing systems involve huge data exchange across various sections of the processing system. To facilitate this, Network-on-Chip (NoC) serve as a crucial infrastructure that connect the processing cores to memory, peripherals, etc. The system could be put at great risk should the NoC system become compromised. The NoCs are used in multi/many-core processors; this domain is experiencing increased threats because of Hardware Trojan (HT) embedded in the multi core processing systems due to the presence of third-party entities in the system-on-chip (SoC) design pipeline. Protecting user and system level privacy becomes important in such multi core systems to enable trust. By embedding a hardware Trojan (HT) in a NoC, the adversary can snoop on important insights regarding the applications executing on the system or the user profile information. An attack of such calibre can compromise privacy thereby enabling more advanced attack on the entire system. This work demonstrates the capability of a traffic analysis attack when a few HTs are embedded in the NoC switches of a multi/many-core processor. The attack is capable of exposing sensitive information to an external malicious attacker who can then analyze the payload data with sophisticated machine learning (ML) techniques to infer the applications executing on the system. We also evaluate the performance of a generative adversarial network (GAN) strengthened attacker model that offers more robustness for data paucity scenarios. We propose a Simulated Annealing-based randomized routing algorithm based defense for NoCs thus thwarting the attack. The results demonstrate that the proposed randomized routing algorithm could reduce the accuracy of identifying user profiles by the attacker from >98% to