HTDet: A clustering method using information entropy for hardware Trojan detection

Lu, Renjie; Shen, Haihua; Feng, Zhihua; Li, Huawei; Zhao, Wei; Li, Xiaowei

doi:10.26599/tst.2019.9010047

Cited by 31 publications

(20 citation statements)

References 32 publications

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“…Since it is based on heuristic of using statistical model to estimate the signal correlations of gate-level netlist, it neither need the circuit to be brought to the triggering state, nor the effect of the Trojan payload to be propagated and observed at the output. This idea was inherited in [82], where the authors directly apply the transition probability as the feature for analysis. To address the order sensitivity problem, a stacked long short-term memory network is designed to build a robust HT detection model.…”

Section: Ml-based Heuristic Analysis For Detection Of Vulnerabilitiesmentioning

confidence: 99%

A Survey on Hardware Vulnerability Analysis Using Machine Learning

Pan

Mishra

2022

IEEE Access

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Electronic systems rely on efficient hardware, popularly known as system-on-chip (SoC), to support its core functionalities. A typical SoC consists of diverse components gathered from third-party vendors to reduce SoC design cost and meet time-to-market constraints. Unfortunately, the participation of third-party companies in global supply chain introduces potential security vulnerabilities. There is a critical need to efficiently detect and mitigate hardware vulnerabilities. Machine learning has been successfully used in hardware security verification as well as development of effective countermeasures. There are recent surveys on hardware Trojan detection using machine learning. To the best of our knowledge, there are no comprehensive surveys on utilization of machine learning techniques for detection and mitigation of a wide variety of hardware vulnerabilities including malicious implants (e.g., hardware Trojans), sidechannel leakage, reverse engineering, and supply-chain vulnerabilities (e.g., counterfeiting, overbuilding and recycling). In this paper, we provide a comprehensive survey of hardware vulnerability analysis using machine learning techniques. Specifically, we discuss how existing approaches effectively utilize machine learning algorithms for hardware security verification using simulation-based validation, formal verification as well as side-channel analysis.

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Section: Ml-based Heuristic Analysis For Detection Of Vulnerabilitiesmentioning

confidence: 99%

A Survey on Hardware Vulnerability Analysis Using Machine Learning

Pan

Mishra

2022

IEEE Access

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“…Another referencefree HT detection scheme utilizing testability measures was developed in [30]. Further, information theory-based HT detection approach investigating the relation between transition probability and the information available on a net for unsupervised Trojan detection using density-based clustering algorithm was attempted in [31]. Transition probability and testability measures were further explored in [32] and [33].Limited representative training data resulted in low TPR.…”

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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“…• Sensors: The sensors' data can be used via the adversary penetration in the control platform. This penetration is simply accomplished via Trojan Horse [159] in which the remote systems are employed as hosts. From the acquisition panel, the sensor output is usually within 15 V. For calibrating it alongside the real measurement, acquisition gains using a linear plotting system is utilized.…”

Section: B Vulnerability Analysis Of Cyber-attacks On the Control Ofmentioning

confidence: 99%

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

et al. 2021

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This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

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HTDet: A clustering method using information entropy for hardware Trojan detection

Cited by 31 publications

References 32 publications

A Survey on Hardware Vulnerability Analysis Using Machine Learning

A Survey on Hardware Vulnerability Analysis Using Machine Learning

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

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

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