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Titlebook: Explainable AI for Cybersecurity; Zhixin Pan,Prabhat Mishra Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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發(fā)表于 2025-3-21 17:35:58 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Explainable AI for Cybersecurity
編輯Zhixin Pan,Prabhat Mishra
視頻videohttp://file.papertrans.cn/320/319281/319281.mp4
概述Introduces a wide variety of software and hardware vulnerabilities.Describes solutions for detecting security attacks using explainable AI.Presents a fast and robust framework using hardware accelerat
圖書封面Titlebook: Explainable AI for Cybersecurity;  Zhixin Pan,Prabhat Mishra Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic
描述This book provides a comprehensive overview of security vulnerabilities and state-of-the-art countermeasures using explainable artificial intelligence (AI). Specifically, it describes how explainable AI can be effectively used for detection and mitigation of hardware vulnerabilities (e.g., hardware Trojans) as well as software attacks (e.g., malware and ransomware). It provides insights into the security threats towards machine learning models and presents effective countermeasures. It also explores hardware acceleration of explainable AI algorithms. The reader will be able to comprehend a complete picture of cybersecurity challenges and how to detect them using explainable AI.? This book serves as a single source of reference for students, researchers, engineers, and practitioners for designing secure and trustworthy systems.
出版日期Book 2023
關(guān)鍵詞Explainable Machine Learning; Adversarial Machine Learning; Malware Detection; Hardware Security; Trustw
版次1
doihttps://doi.org/10.1007/978-3-031-46479-9
isbn_softcover978-3-031-46481-2
isbn_ebook978-3-031-46479-9
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:02:19 | 只看該作者
AI Trojan Attacks and Countermeasures145–1153); Pan and Mishra (Automated detection of Spectre and Meltdown attacks using explainable machine learning. In: 2021 IEEE international symposium on hardware oriented security and trust (HOST). IEEE, 2021, pp 24–34); Witharana and Mishra (Speculative load forwarding attack on modern processor
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Santino D. Carnevale,Roberto C. Myersn technique for spectral normalization based on Fourier transform and layer separation. The proposed method provides DNNs with promising security protection while maintaining minimized time cost, which turns SN from a theoretically feasible approach to a practically useful framework. Experimental ev
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Perspectives on Individual Differencesre events in sequential timestamps rather than relying on overall statistics. We utilize long-short term memory as well as ensemble boosting to accelerate the training speed. We investigate both model distillation and the Shapley value analysis to improve the model’s explainability.
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發(fā)表于 2025-3-22 17:10:42 | 只看該作者
Spectre and Meltdown Detection Using Explainable AIre events in sequential timestamps rather than relying on overall statistics. We utilize long-short term memory as well as ensemble boosting to accelerate the training speed. We investigate both model distillation and the Shapley value analysis to improve the model’s explainability.
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