Heterogenous ensemble learning driven multi-parametric assessment model for hardware Trojan detection

Semiconductor technologies have gained wide-spread attention across industries due to its use in varied digital signal processing units, healthcare-devices, industrial communication, science and technologies, entertainment, smart devices etc. However, high design-complexities, untrusted foundries, third-party processors etc., have raised the concern of intrusion, particularly Hardware Trojans. Hardware Trojan, a specifically designed micro-intruder that can cause data breaches, system malfunctions etc. And hence may result disastrous consequences. To alleviate such problems, though different methods have been suggested for Trojan detection; however, pre-manufacturing methods can't guarantee Trojan-free design due to non-trusted Foundries and Third-party IPs. Similarly, post-manufacturing methods like side channel analysis or gate-level netlist are limited due to highly complex design, large structure and unavailability of universal golden design structures. Considering it as motivation, this paper proposes a robust register transfer logic (RTL) analysis assisted multi-parameter-based machine learning-driven Hardware Trojan detection system. We applied delay, power consumption and resource utilization profiles to perform Trojan detection over AES-256 circuits. Aforesaid features were extracted using VIVADO tool, which were further employed for resampling process using SMOTE, SMOTE-ENN, SMOTE-Borderline algorithms. Here, the use of resampling intended to alleviate data-imbalance or skewness. Moreover, it helped retaining sufficiently large features for machine learning based training. The resampled features were processed for feature selection using Significant Predictor Test, Cross-Correlation Test and Principal Component Analysis (PCA), distinctly. The selected features were trained using a novel heterogenous ensemble learning model encompassing a total of 11 base-classifiers belonging to Naive Bayes, Neuro-computing, Decision Tree, Random Forest, Extra Tree classifier and Bagging-AdaBoost algorithms. Applying the maximum voting ensemble, the proposed model performed path-level Trojan detection, where the simulated results affirmed accuracy, F-Measure and Area Under Curve (AUC) of almost 100%, which is higher than any known approaches towards Hardware Trojan detection.