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Advanced Machine Learning for
Hardware Trojan Detection
Leveraging My Expertise for Trustworthy and Explainable Detection Systems
Stanley Wu (Sept. 27, 2023)
Introduction
• Hardware Trojans (HTs) and their security implications
• A Hardware Trojan (HT) is a malicious modification made to the circuitry of an
integrated circuit (IC).
• An HT is defined both by its physical representation (how it looks and is structured
within the circuit) and its behavior (what it does when activated)
• Example:
• Side-Channel Attacks
• Power Analysis, Timing Analysis, EM Analysis
• Physical Attacks on Chip IDs
• Extracting secret keys
2
Figure Source: "Physical Attack Protection of IC Chips for Hardware Security" by Prof. Makoto Nagata, Kobe University
Objective Overview
1. Automated DL-based HT Detection
2. Trustworthiness & Countermeasures
3. 'Self-Aware' & Explainable Framework
4.Integration into EDA Tools
3
Automated DL-based HT Detection
• Deep Learning (DL) and its advantages in pattern recognition
• Example: use DL algorithm automatically to extract circuit netlists features and then
learns the HT detection models
• TensorFlow aids in the design and implementation of DL-based systems.
4
Trustworthiness & Countermeasures
• The critical nature of countermeasures against adversarial attacks on DL models.
• I was looking at real-time monitoring of model predictions.
• This continuous oversight further bolsters our defenses, ensuring our Deep Learning
models remain trustworthy.
5
'Self-Aware' HT Detection with
Explainable AI
• Explainable AI (XAI) is crucial.
• As we immerse ourselves deeper into the hardware security, a concept that
stands out and demands our attention is 'Self-Aware' Hardware Trojan Detection.
• Artificial Intelligence, especially Deep Learning, often acts as a black box.
• We feed in data, and then we get results, but the internal workings remain
difficult to trace.
• In many complex scenarios, like detecting Hardware Trojans. We need to trust
our tools, and trust comes from understanding.
6
Integration into EDA Tools
• The value of integrating DL-based HT detection systems into EDA tools.
• As we continue our exploration of advanced hardware security, it becomes
important that our solutions need to be seamlessly integrated into the tools that
shape the very foundation of electronic design: the EDA tools.
7
Conclusion & Future Work
• Objectives Recap
• Envisioning the future
8
Questions & Discussion
• Thank you very much for your participation :)
9

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Advanced Machine Learning for Hardware Trojan Detection_v2.pdf

  • 1. Advanced Machine Learning for Hardware Trojan Detection Leveraging My Expertise for Trustworthy and Explainable Detection Systems Stanley Wu (Sept. 27, 2023)
  • 2. Introduction • Hardware Trojans (HTs) and their security implications • A Hardware Trojan (HT) is a malicious modification made to the circuitry of an integrated circuit (IC). • An HT is defined both by its physical representation (how it looks and is structured within the circuit) and its behavior (what it does when activated) • Example: • Side-Channel Attacks • Power Analysis, Timing Analysis, EM Analysis • Physical Attacks on Chip IDs • Extracting secret keys 2 Figure Source: "Physical Attack Protection of IC Chips for Hardware Security" by Prof. Makoto Nagata, Kobe University
  • 3. Objective Overview 1. Automated DL-based HT Detection 2. Trustworthiness & Countermeasures 3. 'Self-Aware' & Explainable Framework 4.Integration into EDA Tools 3
  • 4. Automated DL-based HT Detection • Deep Learning (DL) and its advantages in pattern recognition • Example: use DL algorithm automatically to extract circuit netlists features and then learns the HT detection models • TensorFlow aids in the design and implementation of DL-based systems. 4
  • 5. Trustworthiness & Countermeasures • The critical nature of countermeasures against adversarial attacks on DL models. • I was looking at real-time monitoring of model predictions. • This continuous oversight further bolsters our defenses, ensuring our Deep Learning models remain trustworthy. 5
  • 6. 'Self-Aware' HT Detection with Explainable AI • Explainable AI (XAI) is crucial. • As we immerse ourselves deeper into the hardware security, a concept that stands out and demands our attention is 'Self-Aware' Hardware Trojan Detection. • Artificial Intelligence, especially Deep Learning, often acts as a black box. • We feed in data, and then we get results, but the internal workings remain difficult to trace. • In many complex scenarios, like detecting Hardware Trojans. We need to trust our tools, and trust comes from understanding. 6
  • 7. Integration into EDA Tools • The value of integrating DL-based HT detection systems into EDA tools. • As we continue our exploration of advanced hardware security, it becomes important that our solutions need to be seamlessly integrated into the tools that shape the very foundation of electronic design: the EDA tools. 7
  • 8. Conclusion & Future Work • Objectives Recap • Envisioning the future 8
  • 9. Questions & Discussion • Thank you very much for your participation :) 9