International Association
for Cryptologic Research

In recent years, deep learning has become an attractive ingredient to side-channel analysis (SCA) due to its potential to improve the success probability or enhance the performance of certain frequently executed tasks. One task that is commonly assisted by machine learning techniques is the profiling of a device’s leakage behavior in order to carry out a template attack. At CHES 2019, deep learning has also been applied to non-profiled scenarios for the first time, extending its reach within SCA beyond template attacks. The proposed method, called DDLA, has some tempting advantages over traditional SCA due to merits inherited from (convolutional) neural networks. Most notably, it greatly reduces the need for pre-processing steps< when the SCA traces are misaligned or when the leakage is of a multivariate nature. However, similar to traditional attack scenarios the success of this approach highly depends on the correct choice of a leakage model and the intermediate value to target. In this work we explore, for the first time in literature, whether deep learning can similarly be used as an instrument to advance another crucial (non-profiled) discipline of SCA which is inherently independent of leakage models and targeted intermediates, namely leakage assessment. In fact, given the simple classification-based nature of common leakage assessment techniques, in particular distinguishing two groups fixed-vs-random or fixed-vs-fixed, it comes as a surprise that machine learning has not been brought into this context, yet. Our contribution is the development of the first full leakage assessment methodology based on deep learning. It gives the evaluator the freedom to not worry about location, alignment and statistical order of the leakages and easily covers multivariate and horizontal patterns as well. We test our approach against a number of case studies based on FPGA, ASIC and μC implementations of the PRESENT block cipher, equipped with state-of-the-art SCA countermeasures. Our results clearly show that the proposed methodology and network structures are robust across all case studies and outperform the classical detection approaches (t-test and X2-test) in all considered scenarios.

The effort in reducing the area of AES implementations has largely been focused on application-specific integrated circuits (ASICs) in which a tower field construction leads to a small design of the AES S-box. In contrast, a naive implementation of the AES S-box has been the status-quo on field-programmable gate arrays (FPGAs). A similar discrepancy holds for masking schemes—a well-known side-channel analysis countermeasure—which are commonly optimized to achieve minimal area in ASICs. In this paper, we demonstrate a representation of the AES S-box exploiting rotational symmetry which leads to a 50% reduction in the area footprint on FPGA devices. We present new AES implementations which improve on the state-of-the-art and explore various trade-offs between area and latency. For instance, at the cost of increasing 4.5 times the latency, one of our design variants requires 25% less look-up tables (LUTs) than the smallest known AES on Xilinx FPGAs by Sasdrich and Güneysu at ASAP 2016. We further explore the protection of such implementations against side-channel attacks. We introduce a generic methodology for masking any n -bit Boolean functions of degree t with protection order d . The methodology is exact for first-order and heuristic for higher orders. Its application to our new construction of the AES S-box allows us to improve previous results and introduce the smallest first-order masked AES implementation on Xilinx FPGAs, to date.

The effort in reducing the area of AES implementations has largely been focused on Application-Specific Integrated Circuits (ASICs) in which a tower field construction leads to a small design of the AES S-box. In contrast, a naïve implementation of the AES S-box has been the status-quo on Field-Programmable Gate Arrays (FPGAs). A similar discrepancy holds for masking schemes – a wellknown side-channel analysis countermeasure – which are commonly optimized to achieve minimal area in ASICs.In this paper we demonstrate a representation of the AES S-box exploiting rotational symmetry which leads to a 50% reduction of the area footprint on FPGA devices. We present new AES implementations which improve on the state of the art and explore various trade-offs between area and latency. For instance, at the cost of increasing 4.5 times the latency, one of our design variants requires 25% less look-up tables (LUTs) than the smallest known AES on Xilinx FPGAs by Sasdrich and Güneysu at ASAP 2016. We further explore the protection of such implementations against first-order side-channel analysis attacks. Targeting the small area footprint on FPGAs, we introduce a heuristic-based algorithm to find a masking of a given function with d + 1 shares. Its application to our new construction of the AES S-box allows us to introduce the smallest masked AES implementation on Xilinx FPGAs, to-date.