An In-Depth Investigation on Hardware Trojans Testing Into Cyber Security Concerns Using Machine Learning Models
S. Hariharasitaraman, Ajinkya Bhanpurkar, Mohan Singh, Nilamadhab Mishra · Advances in information security, privacy, and ethics book series · 2024
The current study comprehensively investigates hardware Trojan testing using traditional and machine learning models. It highlights the challenge posed by the uniqueness of each Hardware Trojan and emphasizes the need for reliable solutions. The study recommends using machine learning models with feature extraction to detect patterns in different Hardware Trojans. However, the study also identifies limitations in the existing models, including reliance on the XGBoost algorithm. The study suggests exploring hybrid mechanisms and newer languages, such as LightBGM, for model development as potential areas for further research. The study also underscores the significance of addressing security concerns in IoT technology.