Side-Channel Attacks Based on Collaborative Learning

• 作者: Biao Liu Huamin Feng Jiali Li Yang Pan Zhao Ding
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: Side-channel ATTACKS Supervised LEARNING COLLABORATIVE LEARNING POWER TRACE
• 年，卷（期）: 2017,（1）
• 页码范围: 139-141
• 页数: 3页
• 分类号: C5

摘要

Side-channel attacks based on supervised learning require that the attacker have complete control over the cryptographic device and obtain a large number of labeled power traces.However,in real life,this requirement is usually not met.In this paper,an attack algorithm based on collaborative learning is proposed.The algorithm only needs to use a small number of labeled power traces to cooperate with the unlabeled power trace to realize the attack to cryptographic device.By experimenting with the DPA contest V4 dataset,the results show that the algorithm can improve the accuracy by about 20%compared with the pure supervised learning in the case of using only 10 labeled power traces.