IP-Tag: Tag-Based Runtime 3PIP Hardware Trojan Detection in SoC Platforms

The prevalence of hardware trojans (HTs) poses a significant threat to the integrity and security of Integrated Circuits (ICs). Rule-based Hardware Trojan Detection (HTD) techniques are limited in their effectiveness and scalability. Many domains such as Natural Language Processing (NLP), and cybersecurity are experiencing a surge in automation with the aid of modern Generative Artificial Intelligence (GenAI) techniques such as Generative pre-trained transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), which are applied in Large Language Models (LLMs). In HTD, transformers have only recently started to receive traction. This paper proposes a novel non-destructive golden-chip free transformer-based HTD framework. The proposed framework is applied to Power Side-Channel (PSC) data. Modern generative AI techniques such as GPT, BERT, and transformers are exploited to solve the HTD problem. The proposed framework combines the power of transformer-based networks with time-series side-channel analysis to achieve efficient and accurate HTD. The side-channel data are processed by different transformer networks, including GPT, BERT, and full transformer models to classify the trojan into three main categories: Enabled, Disabled, and Triggered HTs. The proposed framework effectively analyses side-channel measurements, accurately detecting abnormal IC behaviours. The experimental results demonstrated promising and superior performance, achieving an accuracy of 87.74% in HT detection compared with existing frameworks.

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Cracking the Core: Hardware Vulnerabilities in Android Devices Unveiled

Muñoz

2024

Electronics

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As Android devices become more prevalent, their security risks extend beyond software vulnerabilities to include critical hardware weaknesses. This paper provides a comprehensive and systematic review of hardware-related vulnerabilities in Android systems, which can bypass even the most sophisticated software defenses. We compile and analyze an extensive range of reported vulnerabilities, introducing a novel categorization framework to facilitate a deeper understanding of these risks, classified by affected hardware components, vulnerability type, and the potential impact on system security. The paper addresses key areas such as memory management flaws, side-channel attacks, insecure system-on-chip (SoC) resource allocation, and cryptographic vulnerabilities. In addition, it examines feasible countermeasures, including hardware-backed encryption, secure boot mechanisms, and trusted execution environments (TEEs), to mitigate the risks posed by these hardware threats. By contextualizing hardware vulnerabilities within the broader security architecture of Android devices, this review emphasizes the importance of hardware security in ensuring system integrity and resilience. The findings serve as a valuable resource for both researchers and security professionals, offering insights into the development of more robust defenses against the emerging hardware-based threats faced by Android devices.

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IP-Tag: Tag-Based Runtime 3PIP Hardware Trojan Detection in SoC Platforms

Cited by 3 publications

References 24 publications

Deep transfer learning approach for digital circuits vulnerability analysis

Deep transfer learning approach for digital circuits vulnerability analysis

Improving Hardware Trojan Detection with Transformer-Based Power Analysis

Cracking the Core: Hardware Vulnerabilities in Android Devices Unveiled

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