The globalized semiconductor supply chain has made Hardware Trojans (HT) a significant security threat to embedded systems, necessitating the design of efficient and adaptable detection mechanisms. Despite promising machine learning-based HT detection techniques in the literature, they suffer from ad hoc feature selection and the lack of adaptivity, all of which hinder their effectiveness across diverse HT attacks. In this paper, we propose SAND, a self-supervised and adaptive NAS-driven framework for efficient HT detection. Specifically, this paper makes three key contributions. (1) We leverage self-supervised learning (SSL) to enable automated feature extraction, eliminating the dependency on manually engineered features. (2) SAND integrates neural architecture search (NAS) to dynamically optimize the downstream classifier, allowing for seamless adaptation to unseen benchmarks with minimal fine-tuning. (3) Experimental results show that SAND achieves a significant improvement in detection accuracy (up to 18.3%) over state-of-the-art methods, exhibits high resilience against evasive Trojans, and demonstrates strong generalization.

Ransomware has emerged as a severe privacy threat, leading to significant financial and data losses worldwide. Traditional detection methods, including static signature-based detection and dynamic behavior-based analysis, have shown limitations in effectively identifying and mitigating ever-evolving ransomware attacks. In this paper, we present a machine learning-based framework that integrates both software-level scanning along with hardware-level microprocessor activity monitoring to enhance detection performance. Specifically, this paper offers three important contributions. (1) The proposed method incorporates adversarial training to address the weaknesses of conventional static analysis against obfuscation, along with a hardware-assisted dynamic analysis to reduce detection latency. (2) The proposed method employs a neural architecture search (NAS) algorithm to automate the optimization of machine learning models, significantly boosting generalizability. (3) Experimental results demonstrates that our proposed method improves detection accuracy by up to 10.2% and reduces detection latency by up to 4.8x speedup compared to existing approaches.

Ransomware has become a critical threat to cybersecurity due to its rapid evolution, the necessity for early detection, and growing diversity, posing significant challenges to traditional detection methods. While AI-based approaches had been proposed by prior works to assist ransomware detection, existing methods suffer from three major limitations, ad-hoc feature dependencies, delayed response, and limited adaptability to unseen variants. In this paper, we propose a framework that integrates self-supervised contrastive learning with neural architecture search (NAS) to address these challenges. Specifically, this paper offers three important contributions. (1) We design a contrastive learning framework that incorporates hardware performance counters (HPC) to analyze the runtime behavior of target ransomware. (2) We introduce a customized loss function that encourages early-stage detection of malicious activity, and significantly reduces the detection latency. (3) We deploy a neural architecture search (NAS) framework to automatically construct adaptive model architectures, allowing the detector to flexibly align with unseen ransomware variants.