Paperback. The book Anatomy of IoT Botnets and Detection Methods delves into the evolving landscape of cybersecurity threats associated with the Internet of Things (IoT), specifically focusing on the anatomy, behavior, and detection of IoT-based botnets. As IoT devices proliferate in both consumer and industrial settings, their inherent vulnerabilitiessuch as weak authentication, limited processing power, and lack of regular updatesmake them prime targets for attackers. The book begins by exploring how IoT botnets are formed, highlighting key attack vectors such as malware propagation, command, and control (C&C) mechanisms, and commonly exploited protocols such as Telnet and UPnP. Notable case studies, including the Mirai and Mozi botnets, illustrate real-world impacts, emphasizing the scale and damage these threats can inflict. The core of the book then transitions into detection methodologies, covering both traditional and AI-driven approaches. Techniques such as signature-based detection, anomaly detection using machine learning, network traffic analysis, and honeypot deployment are thoroughly examined. The authors also address the challenges in detecting IoT botnets, including encrypted traffic, device heterogeneity, and low visibility in resource-constrained devices. Furthermore, the book emphasizes the importance of proactive defense strategies, such as firmware hardening, secure boot mechanisms, and real-time behavioral analytics. It underscores the role of collaborative intelligence sharing among stakeholders to enhance detection capabilities. By integrating theoretical concepts with practical insights and current research trends, the book provides a comprehensive guide for researchers, cybersecurity professionals, and IoT developers aiming to understand and counteract botnet threats. Ultimately, Anatomy of IoT Botnet and Detection Methods serves as a crucial resource for strengthening the cybersecurity posture of IoT ecosystems through informed detection and mitigation practices. The content of the book is categorized into the following sub-sections: Introduction to IoT and Botnets: An overview of IoT technology, its adoption, and the rising threat of botnets. IoT Device Vulnerabilities: Analysis of common security weaknesses in IoT devices that cybercriminals exploit. Botnet Architecture: Detailed examination of how IoT botnets are structured, including command-and-control mechanisms. Infection and Propagation: Methods used by attackers to spread malware across IoT networks. Detection Techniques: Overview of current detection methods, including anomaly detection, signature-based approaches, and machine learning. Mitigation Strategies: Practical advice on how to secure IoT devices, including best practices for manufacturers and users. Case Studies: Real-world examples of IoT botnet attacks and their impact.The book concludes with a discussion on the future of IoT security, emphasizing the need for continuous innovation in detection and prevention methods. The book Anatomy of IoT Botnets and Detection Methods delves into the evolving landscape of cybersecurity threats associated with the Internet of Things (IoT), specifically focusing on the anatomy, behavior, and detection of IoT-based botnets. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Anatomy of IoT Botnets and Detection Methods (Paperback)
Editor: Taylor & Francis Ltd, London
Fecha de publicaci�n: 2025
Idioma: Ingl�s
ISBN 10: 1041051352
ISBN 13: 9781041051350
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

The book Anatomy of IoT Botnets and Detection Methods delves into the evolving landscape of cybersecurity threats associated with the Internet of Things (IoT), specifically focusing on the anatomy, behavior, and detection of IoT-based botnets. As IoT devices proliferate in both consumer and industrial settings, their inherent vulnerabilities―such as weak authentication, limited processing power, and lack of regular updates―make them prime targets for attackers. The book begins by exploring how IoT botnets are formed, highlighting key attack vectors such as malware propagation, command, and control (C&C) mechanisms, and commonly exploited protocols such as Telnet and UPnP. Notable case studies, including the Mirai and Mozi botnets, illustrate real-world impacts, emphasizing the scale and damage these threats can inflict. The core of the book then transitions into detection methodologies, covering both traditional and AI-driven approaches. Techniques such as signature-based detection, anomaly detection using machine learning, network traffic analysis, and honeypot deployment are thoroughly examined. The authors also address the challenges in detecting IoT botnets, including encrypted traffic, device heterogeneity, and low visibility in resource-constrained devices. Furthermore, the book emphasizes the importance of proactive defense strategies, such as firmware hardening, secure boot mechanisms, and real-time behavioral analytics. It underscores the role of collaborative intelligence sharing among stakeholders to enhance detection capabilities. By integrating theoretical concepts with practical insights and current research trends, the book provides a comprehensive guide for researchers, cybersecurity professionals, and IoT developers aiming to understand and counteract botnet threats. Ultimately, Anatomy of IoT Botnet and Detection Methods serves as a crucial resource for strengthening the cybersecurity posture of IoT ecosystems through informed detection and mitigation practices. The content of the book is categorized into the following sub-sections:

Introduction to IoT and Botnets: An overview of IoT technology, its adoption, and the rising threat of botnets.
IoT Device Vulnerabilities: Analysis of common security weaknesses in IoT devices that cybercriminals exploit.
Botnet Architecture: Detailed examination of how IoT botnets are structured, including command-and-control mechanisms.
Infection and Propagation: Methods used by attackers to spread malware across IoT networks.
Detection Techniques: Overview of current detection methods, including anomaly detection, signature-based approaches, and machine learning.
Mitigation Strategies: Practical advice on how to secure IoT devices, including best practices for manufacturers and users.
Case Studies: Real-world examples of IoT botnet attacks and their impact.

The book concludes with a discussion on the future of IoT security, emphasizing the need for continuous innovation in detection and prevention methods.

Acerca del autor

Umang Garg, PhD, is working as an Associate professor in department of Computer Science and Engineering, MITADT University, Pune, Maharashtra, India. He is committed to advance both research and teaching in the field of Security. He has combined research and teaching experience of over 14 years and done a significant contribution towards the research community working in IoT security using Machine learning. Dr. Garg has done PhD (IoT Security), M.Tech (CSE), and B.Tech (CSE) for his academic Pensiveness. He has published more than 40 research articles with reputed journals and highly indexed conferences. He is Member of CSI and IEEE.

Neha Gupta, PhD, working as an Assistant professor in department of Computer Science and Engineering, MITADT University, Pune, Maharashtra, India. She is committed to advance both research and teaching in the field of IoT and Healthcare applications. She has combined research and teaching experience of over 12 years and done a significant contribution towards the research community working in IoT healthcare applications. Dr. Gupta has done PhD (5G-IoT in Healthcare), M.Tech, and B.Tech for her academic Pensiveness.

Rajesh Singh, PhD, is associated with Uttaranchal University as Professor and Director (Research & Innovation) with more than twenty years of experience in academics and research. His area of expertise includes embedded systems, robotics, wireless sensor networks, Internet of Things and Machine Learning. He has been honored as keynote speakers and session chair to international/national conferences, faculty development programs, workshops and webinars. He has filed more than 500 IPR, including 100+ Indian & International patent grants, 5 PCT and published more than 439 research papers in SCI/Scopus journals. He has authored/edited forty three books in the area of Embedded Systems and Internet of Things with reputed publishers like CRC/Taylor & Francis, Springer, Narosa, NIPA, River Publishers, Bentham Science, IGI Global, NOVA Science etc. He has been featured by Indian and International media for smart systems and devices designed by him including-OBDAS, E-Parirakshak, Kawach, 20Sec4Life, Ally, Alithis, CT scan Diagnosis, among which five are designed to prevent COVID-19 as per WHO guidelines.

Anita Gehlot, PhD, is associated with Uttaranchal University as Professor and Head – Research & Innovation with more than sixteen years of experience. Her area of expertise includes embedded systems, wireless sensor networks, Internet of Things, Artificial Intelligence and Automation. She has been featured among top ten inventors for ten years 2010-2020, by Clarivate Analytics in “India’s Innovation Synopsis” in March 2021. She has filed more than five hundred patents including more than hundered Indian & International patent grants, 5 PCT and published more than three hundred and fifty research papers in SCI/Scopus journals. Five scholars have been awarded with PhD under her supervision.

Ankur Dumka, PhD, is working as associate professor from past 6 years and holding various administrative post. He was coordinator and member of smart city dehradun for drafting of proposal. He is having more than 140 research papers with more than 60 sci and Scopus journal publication and more than 120 Scopus publication. He has contributed 8 authored books with reputed international publishers, 15 book chapters, 8 patents granted under his name and 7 published, he is handling 1 research project of more than 10 lakh and 1 consultancy project of govt. Medical college.

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