Security of Biochip Cyberphysical Systems 1st ed. 2022 Edition

Security of Biochip Cyberphysical Systems 1st ed. 2022 Edition book cover

Security of Biochip Cyberphysical Systems 1st ed. 2022 Edition

Author(s): Shayan Mohammed (Author), Sukanta Bhattacharjee (Author), Yong-Ak Song (Author), Krishnendu Chakrabarty (Author), Ramesh Karri (Author)

  • Publisher: Springer
  • Publication Date: 30 Mar. 2023
  • Edition: 1st ed. 2022
  • Language: English
  • Print length: 140 pages
  • ISBN-10: 3030932761
  • ISBN-13: 9783030932763

Book Description

This book provides readers with a valuable guide to understanding security and the interplay of computer science, microfluidics, and biochemistry in a biochip cyberphysical system (CPS). The authors uncover new, potential threat and trust-issues to address, as this emerging technology is poised to be adapted at a large scale. Readers will learn how to secure biochip CPS by leveraging the available resources in different application contexts, as well as how to ensure intellectual property (IP) is protected against theft and counterfeits. This book enables secure biochip CPS design by helping bridge the knowledge gap at the intersection of the multi-disciplinary technology that drives biochip CPS.

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From the Back Cover

This book provides readers with a valuable guide to understanding security and the interplay of computer science, microfluidics, and biochemistry in a biochip cyberphysical system (CPS). The authors uncover new, potential threat and trust-issues to address, as this emerging technology is poised to be adapted at a large scale. Readers will learn how to secure biochip CPS by leveraging the available resources in different application contexts, as well as how to ensure intellectual property (IP) is protected against theft and counterfeits. This book enables secure biochip CPS design by helping bridge the knowledge gap at the intersection of the multi-disciplinary technology that drives biochip CPS.
Provides tools for security analysis and verification: a machine learning (ML) framework;

Paints the threat landscape of the biochip CPS, describing the threat models – the who, the how, and the why;

Uses real case studies to describe models of tampering attack, microfluidic trojan attack.

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