Prabhat Mishra University of Florida United States 3 (Southeastern U.S.) Email 2023 2024 Talk(s): Securing Hardware for Designing Trustworthy Systems Securing Hardware for Designing Trustworthy Systems × System-on-Chip (SoC) is the brain behind computing and communication in a wide variety of embedded systems. Reusable hardware Intellectual Property (IP) based SoC design has emerged as a pervasive design practice in the industry to dramatically reduce SoC design and verification cost while meeting aggressive time-to-market constraints. Growing reliance on these pre-verified hardware IPs, often gathered from untrusted third-party vendors, severely affects the security and trustworthiness of computing platforms. It is crucial to evaluate the integrity and trustworthiness of third-party IPs for designing trustworthy systems. In this talk, I will introduce a wide variety of hardware security vulnerabilities, design-for-security solutions, and possible attacks and countermeasures. I will briefly describe how the complementary abilities of simulation-based validation, formal verification as well as side channel analysis can be effectively utilized for comprehensive SoC security and trust validation. Design Automation for Quantum Computing Design Automation for Quantum Computing × Quantum technologies offer promising advantages over classical counterparts in a variety of tasks, including faster computation, secure communication, and high-precision sensors. This tutorial will provide a comprehensive overview of both fundamental concepts and recent advances in design automation of quantum computing. This tutorial will consist of three parts. The first part will describe the fundamentals of quantum computing from the perspective of computer scientists and computer engineers. The second part will describe design automation tools and flows to enable robust quantum computing. It will discuss various quantum algorithms as well as automated methods to map these algorithms on quantum computers. Specifically, it will cover specification languages, quantum compilation, control generation, quantum state preparation, quantum error correction, quantum machine learning, quantum measurement, as well as validation of quantum systems. Finally, it will outline the importance of noise modeling and mitigation methods in todayís noisy intermediate scale quantum computers. Explainable AI for Cybersecurity Explainable AI for Cybersecurity × This tutorial will provide a comprehensive overview of security attacks as well as detection techniques using explainable AI. Specifically, the tutorial will consist of six parts. The first part will outline a wide variety of software and hardware security threats and vulnerabilities. The second part will cover various machine learning algorithms, including decision tree, random forest, deep neural network, recurrent neural network, unsupervised learning, zero-shot learning, and reinforcement learning. The third part will introduce explainable AI algorithms to interpret machine learning modelsí behaviors in a human-understandable way, using model distillation, Shapley value analysis, and integrated gradients. The fourth part will discuss state-of-the-art attack detection using explainable AI. The fifth part will cover how to enable hardware acceleration of explainable AI models for real-time vulnerability detection. Finally, it will discuss the security threats toward machine learning models (adversarial attack, poisoning attack, and AI Trojan attack), and effective countermeasures to design robust AI models.
