Instruction Flow-based Detectors against Fault Injection Attacks

Abstract

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), content addressable memory (CAM), and Bloom filter (BF)) that detect fault attacks against software RSA decryption. After conducting experiments with various fault models, we observed that the CAM provides the best detection rate, the RNN provides the most software/application flexibility, and the BF is a middle ground between the two. Regardless, all of them exhibit robustness against faults targeted at them, and obtain a very high detection rate when faults change instructions altogether. This affirms the validity of monitoring the integrity of the instruction flow as a strong countermeasure against any type of fault attack.