Intelligent Machine Homicide - Breaking Cryptographic Devices Using Support Vector Machines

In this contribution we propose the so-called SVM attack, a profiling based side channel attack, which uses the machine learning algorithm support vector machines (SVM) in order to recover a cryptographic secret. We compare the SVM attack to the template attack by evaluating the number of required traces in the attack phase to achieve a fixed guessing entropy. In order to highlight the benefits of the SVM attack, we perform the comparison for power traces with a varying noise level and vary the size of the profiling base. Our experiments indicate that due to the generalization of SVM the SVM attack is able to recover the key using a smaller profiling base than the template attack. Thus, the SVM attack counters the main drawback of the template attack, i.e. a huge profiling base.