Enhanced Gradual-N-Justification Methodology with Local Outlier Factor (LOF) for Hardware Trojan Detection

Security and trust of any electronic system is one of the major concern in the present era of globalization. The malicious or unexpected modifications capable enough of accessing the system to change its functionality and weaken the system: Hardware Trojans. However, a methodology that is reference-free, proficient, and experience high-false positives is greatly encouraged. The study involves: a reference-free hardware detection analysis, gradual-N-justification methodology, and local outlier factor. GNJ methodology is an extensible linear algorithm that produces a list of suspicious signals, detects the HT, and reduces the false-positive rates. Local outlier factor adds a depth to the study by classifying the data points based on the local density, reduces the suspicious signals, and subsequently the number of iterations required to bring out the most suspicious signals. The proposed methodology does not fail to bring out any non-maskable Trojan if inserted into the circuit. Therefore, the proposed methodology extracts the most suspicious signals from a list of suspicious signals with high accuracy and less time.