LoS Trick: Countermeasure against CPA for DNN Models Using Loss of Significance in Multiply-Accumulate Operations

The recent advancements in deep neural network (DNN) models have revolutionized image recognition, natural language processing, and healthcare. However, deploying DNN models in IoT devices introduces significant security risks, particularly side-channel attacks such as correlation power analysis (CPA). In this paper, we introduce a novel countermeasure named loss of significance (LoS) trick LoS trick is designed to protect DNN models from CPA attacks by reducing signal-to-noise ratio (SNR) during the multiply-accumulate operations. We reduce information leakage in power consumption traces by adding large absolute values in the initial stages of multiply-accumulate operations. LoS trick increases the difficulty of obtaining accurate weight parameters. This paper details the theoretical foundation of LoS trick and its implementation methodology and validates its effectiveness through simulations. The results demonstrate that LoS trick outperforms conventional countermeasures, providing a scalable solution to enhance the security of DNN models in IoT environments.