セキュアブロック暗号に対する網羅的な深層学習電力解析とその評価

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2024/01/01

タイトル(英語): Exhaustive Deep Learning Power Analysis for Secure Block Ciphers and Its Evaluation

著者名: 竹本　修（名城大学大学院理工学研究科），池崎　良哉（名城大学大学院理工学研究科），野崎　佑典（名城大学情報工学部），吉川　雅弥（名城大学情報工学部）

著者名(英語): Shu Takemoto (Graduate School of Science and Technology, Meijo University), Yoshiya Ikezaki (Graduate School of Science and Technology, Meijo University), Yusuke Nozaki (Faculty of Information Engineering, Meijo University), Masaya Yoshikawa (Faculty of Information Engineering, Meijo University)

キーワード: サイバーセキュリティ，深層学習，軽量ブロック暗号，電力解析，耐タンパ性　　cyber security，deep learning，lightweight block cipher，power analysis，tamper resistance

要約(英語): In order to realize a sustainable society that integrates cyber and physical space (Society 5.0), it is important to construct secure Cyber-Physical System (CPS). Devices in industrial systems must be energy efficient for environmental protection and low latency for production efficiency. On the other hand, CPS has issues such as huge energy consumption of the entire system and delays caused by communication processing due to frequent network connections of many devices. Lightweight block ciphers are one of the key technologies to solve these problems and improve the confidentiality of highly secret communication data. PRINCE with low latency and Midori128 with low energy operation are both computationally secure. Furthermore, they have been reported to have tamper-resistant circuits with improved security against the threat of power analysis attacks, which use the power consumption of cryptographic operations to guess secret keys. However, deep learning power analysis attacks have been proposed in recent years, focusing on deep learning, which has dramatically improved performance. Therefore, it is very important to evaluate the tamper resistance of PRINCE and Midori128. Against this background, this study examines the threat of deep learning power analysis attack methods against several implementations of PRINCE and Midori128. The proposed method achieves efficient analysis by generating deep learning training data oriented to implementation types and target ciphers. Evaluation experiments using actual equipment showed that the proposed method is able to analyze all partial keys of tamper-resistant circuits, which were difficult to analyze with conventional attack methods.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.144 No.1 （2024）

本誌掲載ページ: 7-14 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/144/1/144_7/_article/-char/ja/