Ml assisted techniques in power side channel analysis for trojan classification

Ml assisted techniques in power side channel analysis for trojan classification

연구 분야: Cryptography

학회: Cluster Computing

Hardware Trojans (HTs) are one of the emerging malicious hardware modification attacks that have become a critical threat to the integrity, reliability, security, and trustworthiness of integrated circuits (ICs) applications. These deliberately hidden malicious entities can be inserted into the IC during manufacturing or design, potentially leading to the leakage of secret information or the deactivation or destruction of the entire system that relies on the IC hardware chips. Localizing and detecting hardware Trojans is becoming increasingly challenging as these threats are deeply embedded and electronic systems continue to grow in complexity. Traditional methods, including physical inspections and functional testing, are increasingly inadequate. They are limited in scope and often fall short when confronted with the advanced designs of modern hardware Trojans.This study aims to improve the identification of hardware Trojans (HTs) in integrated circuits by using advanced machine learning algorithms with a unique dataset of power side-channel signals. Various machine learning techniques, such as Support Vector Machines (SVM), neural networks, and decision trees, are applied to classify and identify HTs accurately. This method combines comprehensive feature extraction and model validation to provide high accuracy and reliability in HT identification. This research makes significant contributions to cybersecurity by providing improved techniques for detecting minor anomalies associated with HTs using advanced machine learning algorithms. The results show potential advances in securing electronic systems against these hidden threats, highlighting the practical significance and necessity of our work in maintaining a wide range of applications from consumer electronics to national infrastructure.