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FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks.

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A self-learning framework to detect the intruded integrated circuits., , , , , and . ISCAS, page 1702-1705. IEEE, (2016)FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack., , , , and . CoRR, (2021)A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems., , , , , and . IEEE Access, (2019)FaDec: A Fast Decision-based Attack for Adversarial Machine Learning., , , , , and . IJCNN, page 1-8. IEEE, (2020)Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks., , , , , and . IJCNN, page 1-8. IEEE, (2020)Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges., , , , , , and . ISVLSI, page 553-559. IEEE, (2019)Hardware Trojan Based Security Issues in Home Area Network: A Testbed Setup., , , , , and . MWSCAS, page 972-975. IEEE, (2018)Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks., , and . CoRR, (2021)MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Resource-Constrained IoT Edge Devices., , , , , and . CoRR, (2020)GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking., , , , , , and . DATE, page 780-785. IEEE, (2021)