Scholarlink

<p>Recent breakthroughs in deep learning-based side-channel analysis (DLSCA) have demonstrated unprecedented cross-device cryptographic profiling capabilities, even against provably secure masking schemes. The evolving DLSCA paradigm is shifting toward practical attacks with minimal prerequisites–neither signal pre-processing nor prior implementation knowledge is required–suggesting that automated AI-driven side-channel analysis will soon become a viable threat model. To investigate the intrinsic vulnerability of masking countermeasures under DLSCA, we construct a configurable leakage framework that simultaneously exposes first- and second-order side-channel emanations. Counterintuitively, deep neural networks tend to converge on biased low-order leakage patterns while systematically overlooking genuine higher-order correlations, a phenomenon we term Feature Extraction Fragility. Leveraging this observation, we introduce BLAM (Biased Leakage Augmentation Methodology), a systematic approach that enhances masking resilience against DLSCA with automated feature extraction. Through theoretical analysis and experimental validation, we show that BLAM strategically distorts leakage profiles to induce networks to learn erroneous low-order patterns. The proposed technique establishes a generic reinforcement framework that can be seamlessly complied with any existing masking design and introducing limited additional resource and speed overhead.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SpoofLearner: Inducing Deceptive Results in Deep Learning SCA via Low-Order Leakage

  • 期刊论文

  • 发表日期：

    2026年4月14日

查看全文
  • Ming Tang,
  • Mengxing Wang

摘要

Recent breakthroughs in deep learning-based side-channel analysis (DLSCA) have demonstrated unprecedented cross-device cryptographic profiling capabilities, even against provably secure masking schemes. The evolving DLSCA paradigm is shifting toward practical attacks with minimal prerequisites–neither signal pre-processing nor prior implementation knowledge is required–suggesting that automated AI-driven side-channel analysis will soon become a viable threat model. To investigate the intrinsic vulnerability of masking countermeasures under DLSCA, we construct a configurable leakage framework that simultaneously exposes first- and second-order side-channel emanations. Counterintuitively, deep neural networks tend to converge on biased low-order leakage patterns while systematically overlooking genuine higher-order correlations, a phenomenon we term Feature Extraction Fragility. Leveraging this observation, we introduce BLAM (Biased Leakage Augmentation Methodology), a systematic approach that enhances masking resilience against DLSCA with automated feature extraction. Through theoretical analysis and experimental validation, we show that BLAM strategically distorts leakage profiles to induce networks to learn erroneous low-order patterns. The proposed technique establishes a generic reinforcement framework that can be seamlessly complied with any existing masking design and introducing limited additional resource and speed overhead.