A highly efficient side channel attack with profiling through relevance-learning on physical leakage information

We propose a Profiling through Relevance-Learning (PRL) technique on Physical Leakage Information (PLI) to extract highly correlated PLI with processed data, as to achieve a highly efficient yet robust Side Channel Attack (SCA). There are four key features in our proposed PRL. First, variance analysis on PLI is implemented to determine the boundary of the clusters and objects of the clusters. Second, the nearest-neighbor k-NN variance clustering is used to reduce the sampling points of PLI by clustering the high variance sampling points and discarding the low variance sampling points of PLI measurements (traces). These clustered sampling points, which are highly correlated with the processed data, contain pertinent leakage information related to the secret key. Third, the information associated with the secret key is spread in several neighboring sampling points with different degrees of leakages. We analytically derive the Key-leakage relevance factor for each clustered sampling point to quantify the degree of leakage associated with the secret key. Fourth, by means of Hebbian learning, a weight proportional to the Key-leakage relevance factor is updated iteratively based on the values of relevance factor and traces of the sampling points. The converged weights which are being assigned to clustered sampling points are linked to their associated PLI to further increase the correlation of the PLI with the processed data. Therefore, the required number of PLI measurements, to reveal the secret key, can be reduced significantly. In addition, we analytically show that the computational complexity of our proposed PRL is O(n) when compared to the reported profiling techniques having O(n ) and O(n ) computational complexities. Based on the experiments of our proposed PRL performed on the PLI of AES-128 algorithm, the results depicting that the sampling points of PLI are reduced 87 percent after the k-NN variance clustering. The converged weight with learning error rate >10 traces, our proposed PRL is ∼2,000× more efficient in performing SCA.Agency for Science, Technology and Research (A*STAR)This research work was supported by Agency for Science, Technology and Research (A STAR), Singapore, under SERC 2013 Public Sector Research Funding, Grant No: SERC1321202098. The authors thank A STAR for the kind funding support