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IMPLEMENTATION OF SIDE-CHANNEL LEAKAGE DETECTION TECHNIQUE BASED ON NORMALIZED INTER-CLASS VARIANCE METHOD

By A. B. Levina and P. S. Borisenko

Abstract

The paper presents anew mathematical method for parasitic signal analyzing. NICV (Normalized Inter-Class Variance) method allows reducing considerably of computing and time expenditure in carrying out side channel attacks. To analyze NICV efficiency mathematical statistics methods and theory of probability were used. The paper presents an algorithm implementing NICV within a developed software package. The main shortcomings of the existing solutions have been taking into consideration during development of the new software: architecture of the presented software is easily extensible for adding new tools; unified format is used for all processing data. NICV was tested on the first round of 64-bit Data Encryption Standard algorithm. To assess the effectiveness two attacks based on differential power analysis and correlation power analysis have been simulated. Another advantage of the package is flexibility in adding of new methods for processing, saving, both original information and its new statuses in the database after carrying out signal processing.Side-Channel Attacks (SCA) are considered as a serious threat for data protected by cryptographic devices. Therefore such devices must be tested for resistance to these attacks. It should be taken into account that SCA are very powerful tool but they require significant computation capacity, especially in case of countermeasures. Presented software package program can help to analyze cryptographic devices on resistance to SCA and implemented NICV method allows decreasing of time and computation costs

Topics: cryptography, side-channel attacks, NICV, Optics. Light, QC350-467, Electronic computers. Computer science, QA75.5-76.95
Publisher: Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
Year: 2016
OAI identifier: oai:doaj.org/article:b0d160d5e26c4c098d11646a249c379b
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