A method to prevent hardware Trojans limiting access to layout resources

Supon, Tareq Muhammad; Seyedbarhagh, Mahsasadat; Rashidzadeh, Rashid; Muscedere, Roberto

doi:10.1016/j.microrel.2021.114212

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…HTD schemes can be classified as pre-silicon and post-silicon detection [19] depending on the scheme applied prior to or after fabrication. Gate-level netlist detection [20], register transfer level (RTL) feature detection [22] and layout level detection [23] constitute HTD at pre-silicon stage. On the other hand, post-silicon detection consists of logical testing [24], [25] and side-channel analysis [26].…”

Section: Related Workmentioning

confidence: 99%

Data Augmented Hardware Trojan Detection Using Label Spreading Algorithm Based Transductive Learning for Edge Computing-Assisted IoT Devices

Sankar

Jayakumar

2022

IEEE Access

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IoT devices handle a large amount of information including sensitive information pertaining to the deployed application. Such a scenario, makes IoT devices susceptible to various attacks. In addition to securing IoT devices, it is equally important to secure communication among devices and with the outside world. RS232 is a common communication protocol used in IoT and embedded devices. Hence ensuring, Trojan detection in RS232 plays a major role in providing secured communication among edge assisted IoT devices. The inclusion of malicious circuits known as hardware Trojans can occur at any stage of the IC design and manufacturing. Existing pre-silicon detection schemes with static features is limited by the number of features that are learned by the detection scheme. In contrast, machine learning allows enhanced Trojan space exploration. Existing machine learning-based Trojan detection consists primarily of supervised algorithms that rely on high-quality labeled datasets for efficient Trojan detection. Unsupervised methods, on the other hand, underperform due to limited training data and severe imbalance within the available data. To handle such a situation, a semi-supervised hardware Trojan detection has been proposed. In this work, permutation importance guided principal component analysis, correlation aware data augmentation, and hyper-parameter optimization using genetic algorithm aid in optimal dataset and model generation. Pseudo label generation using semi-supervised schemes is utilized to handle partially labeled datasets. For the Trust-HUB benchmarks, the proposed methodology achieves an average of 88.48% true positive rate and 95.77% true negative rate which, clearly indicates the effectiveness and feasibility of semi-supervised hardware Trojan detection. INDEX TERMSSemi-supervised algorithm, hardware Trojan detection, correlation-aware data augmentation, hyper-parameter optimization, genetic algorithm, permutation importance, XGBoost I. INTRODUCTION T HE rapid advancement in microelectronic technologies has led to the exploration of cloud computing, big data, artificial intelligence, embedded systems, 5G communication and internet of things (IoT). IoT extends from smart city to smart healthcare including many mission critical systems.IoT framework consists of sensors, actuators and embedded electronic devices that receive, store and transmit data. As per forecast, the number of connected smart devices will reach 75 billion by 2025 [1]. When the number of connected devices grow, there exists a multi-fold increase in the data to be handled. In such a scenario, quality of service (QoS)

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