Test generation using reinforcement learning for delay-based side-channel analysis

Pan, Zhixin; Sheldon, Jennifer; Mishra, Prabhat

doi:10.1145/3400302.3415710

Cited by 33 publications

(5 citation statements)

References 27 publications

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“…For example, researchers have developed an RL based testing algorithm that trains dishonest agents to reveal dangerous behaviours of autonomous cyber-physical systems [49]. An RL based test generation technique has also been devised with the aim of increasing hardware Trojan detection accuracy [53], [54].…”

Section: A Problem Statement and Related Workmentioning

confidence: 99%

Efficient state synchronisation in model-based testing through reinforcement learning

Türker

Hierons

Mousavi

et al. 2021

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Section: A Problem Statement and Related Workmentioning

confidence: 99%

Efficient state synchronisation in model-based testing through reinforcement learning

Türker

Hierons

Mousavi

et al. 2021

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…Recently, the use of RL has received attention in several areas of testing, including Android testing [59], mutation testing [60], online testing [61], and security testing [62], [63], [64]. Except for our past work [40] no approach has been proposed for FSM-based testing.…”

Section: Test Generation Using Reinforcement Learningmentioning

confidence: 99%

“…Within security testing, researchers have developed an RL-based testing algorithm that trains dishonest agents to reveal dangerous behaviours of autonomous cyber-physical systems [64]. In addition, an RL-based test generation technique has been devised to increase hardware Trojan detection accuracy [62], [63].…”

Section: Test Generation Using Reinforcement Learningmentioning

confidence: 99%

Accelerating Finite State Machine-Based Testing Using Reinforcement Learning

Türker,

Hierons,

El-Fakih

et al. 2024

IIEEE Trans. Software Eng.

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“…To address these challenges, Pan et al [100] proposed a test generation method using reinforcement learning for delay-based Trojan detection. This approach utilizes critical path analysis to generate test vectors that can maximize the side-channel sensitivity.…”

Section: Ml-based Side-channel Analysis Of Hardware 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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Test generation using reinforcement learning for delay-based side-channel analysis

Cited by 33 publications

References 27 publications

Efficient state synchronisation in model-based testing through reinforcement learning

Efficient state synchronisation in model-based testing through reinforcement learning

Accelerating Finite State Machine-Based Testing Using Reinforcement Learning

A Survey on Hardware Vulnerability Analysis Using Machine Learning

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