Machine Learning for Embedded System Security by Basel Halak Paperback Book

Description: Machine Learning for Embedded System Security by Basel Halak This book comprehensively covers the state-of-the-art security applications of machine learning techniques. The third part provides an in-depth insight into the principles of malware analysis in embedded systems and describes how the usage of supervised learning techniques provides an effective approach to tackle software vulnerabilities. FORMAT Paperback CONDITION Brand New Publisher Description This book comprehensively covers the state-of-the-art security applications of machine learning techniques. The first part explains the emerging solutions for anti-tamper design, IC Counterfeits detection and hardware Trojan identification. It also explains the latest development of deep-learning-based modeling attacks on physically unclonable functions and outlines the design principles of more resilient PUF architectures. The second discusses the use of machine learning to mitigate the risks of security attacks on cyber-physical systems, with a particular focus on power plants. The third part provides an in-depth insight into the principles of malware analysis in embedded systems and describes how the usage of supervised learning techniques provides an effective approach to tackle software vulnerabilities. Author Biography Dr. Basel Halak is the director of the embedded systems and IoT program at the University of Southampton, a visiting scholar at the Technical University of Kaiserslautern, a visiting professor at the Kazakh-British Technical University, an industrial fellow of the royal academy of engineering, and a national teaching fellow of the Advance Higher Education(HE) Academy. Dr. Halaks publications include over 80-refereed conference and journal papers and authored four books, including the first textbook on Physically Unclonable Functions. His research expertise includes evaluation of the security of hardware devices, development of countermeasures, mathematical formalism of reliability issues in CMOS circuits (e.g. crosstalk, radiation, aging), and the use of fault tolerance techniques to improve the robustness of electronics systems against such issues. Dr. Halak lectures on digital design, Secure Hardware, and Cryptography. Dr. Halak serves on several technical program committees such as HOST, IEEE DATE, IVSW, and DAC. He is an associate editor of IEEE access and an editor of the IET circuit devices and system journal. He is also a member of the hardware security-working group of the World Wide Web Consortium (W3C). Table of Contents Introduction.- Machine Learning for Tamper Detection.- Machine Learning for IC Counterfeit Detection and Prevention.- Machine Learning for Secure PUF Design.- Machine Learning for Malware Analysis.- Machine Learning for Detection of Software Attacks.- Conclusions and Future Opportunities. Details ISBN3030941809 Author Basel Halak Pages 160 Publisher Springer Nature Switzerland AG Year 2023 Edition 1st ISBN-13 9783030941802 Format Paperback Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Edited by Basel Halak ISBN-10 3030941809 UK Release Date 2023-04-23 Edition Description 1st ed. 2022 Publication Date 2023-04-23 Alternative 9783030941772 DEWEY 005.8 Audience Professional & Vocational Illustrations 39 Illustrations, color; 27 Illustrations, black and white; XV, 160 p. 66 illus., 39 illus. in color. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:142027468;

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