This project develops new and promising techniques in the area of side-channel attacks and their corresponding countermeasures. In a side-channel attack, an attacker captures the implementation effects of cryptography, such as power consumption and execution time. A distinctive feature of a side-channel analysis (SCA) attack is that it can reveal a small part of the secret-key. Hence, side-channel attacks avoid the brute-force complexity of cryptanalysis. Using novel side-channel estimation techniques based on Bayesian statistics, the project develops more powerful side-channel attacks. The development of novel side-channel analysis techniques is crucial in order to obtain the best possible countermeasures. The project also develops novel software-oriented countermeasures that more flexible and general than traditional hardware-oriented side-channel countermeasures. The efficiency of side-channel attacks and side-channel countermeasures are evaluated using hardware and software prototyping. The project combines advanced statistical techniques with advanced computer engineering, building synergy between Statistics and Computer Engineering. In the field of Statistics, the Bayesian matching technique can be used for variable selection, a technique that is applicable to related problems in biostatistics, machine learning, data mining, genomics, and other areas with high dimensional data. Project results will be disseminated by distributing open-source prototype implementations, measurement data, and in open publications. A formal training program within the Laboratory for Interdisciplinary Statistical Analysis (LISA) at Virginia Tech is developed to distribute the results of this project to students.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Angelos Keromytis
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United States
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