Leakage Assessment Methodology Research Articles

High-level synthesis, cryptography, and side-channel countermeasures: A comprehensive evaluation

Side-channel attacks pose a severe threat to both software and hardware cryptographic implementations. Current literature presents various countermeasures against these kinds of attacks, based on approaches such as hiding or masking, implemented either in software, or on register–transfer level or gate level in hardware. However, emerging trends in hardware design lean towards a system-level approach, allowing for faster, less error-prone, design process, an efficient hardware/software co-design, or sophisticated validation, verification, and (co)simulation strategies. In this paper, we propose a Boolean masking scheme suitable for high-level synthesis of substitution–permutation network-based encryption. We implement both unprotected and protected PRESENT, AES/Rijndael and Serpent encryption in C language, utilizing the concept of dynamic logic reconfiguration, synthesize it for Xilinx FPGA, and we compare our results regarding time and area utilization. We evaluate the effectiveness of proposed countermeasures using both specific and non-specific t-test leakage assessment methodology. We discuss the leakage assessment results, and we identify and discuss the related limitations of the system-level approach and the high-level synthesis.

DL-LA: Deep Learning Leakage Assessment

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.

CC Meets FIPS: A Hybrid Test Methodology for First Order Side Channel Analysis

Common Criteria (CC) and FIPS 140-3 are two popular side channel testing methodologies. Test Vector Leakage Assessment Methodology ( TVLA ), a potential candidate for FIPS, can detect the presence of side-channel information in leakage measurements. However, TVLA results cannot be used to quantify side-channel vulnerability and it is an open problem to derive its relationship with side channel attack success rate ( SR ), i.e., a common metric for CC. In this paper, we extend the TVLA testing beyond its current scope. Precisely, we derive a concrete relationship between TVLA and signal to noise ratio ( SNR ). The linking of the two metrics allows direct computation of success rate ( SR ) from TVLA for given choice of intermediate variable and leakage model and thus unify these popular side channel detection and evaluation metrics. An end-to-end methodology is proposed, which can be easily automated, to derive attack SR starting from TVLA testing. The methodology works under both univariate and multivariate setting and is capable of quantifying any first order leakage. Detailed experiments have been provided using both simulated traces and real traces on SAKURA-GW platform. Additionally, the proposed methodology is benchmarked against previously published attacks on DPA contest v4.0 traces, followed by extension to jitter based countermeasure. The result shows that the proposed methodology provides a quick estimate of SR without performing actual attacks, thus bridging the gap between CC and FIPS.

Leakage assessment methodology

Evoked by the increasing need to integrate side-channel countermeasures into security-enabled commercial devices, evaluation labs are seeking a standard approach that enables a fast, reliable and robust evaluation of the side-channel vulnerability of the given products. To this end, standardization bodies such as NIST intend to establish a leakage assessment methodology fulfilling these demands. One of such proposals is the Welch’s t test, which is being put forward by Cryptography Research Inc. and is able to relax the dependency between the evaluations and the device’s underlying architecture. In this work, we deeply study the theoretical background of the test’s different flavors and present a roadmap which can be followed by the evaluation labs to efficiently and correctly conduct the tests. More precisely, we express a stable, robust and efficient way to perform the tests at higher orders. Further, we extend the test to multivariate settings and provide details on how to efficiently and rapidly carry out such a multivariate higher-order test. Including a suggested methodology to collect the traces for these tests, we point out practical case studies where different types of t tests can exhibit the leakage of supposedly secure designs.