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Название: Big Data and Edge Intelligence for Enhanced Cyber Defense: Principles and Research
Автор: Ranjit Panigrahi, Victor Hugo C. de Albuquerque, Akash Kumar Bhoi, Hareesha K.S.
Издательство: CRC Press
Серия: Edge AI in Future Computing
Год: 2024
Страниц: 200
Язык: английский
Формат: pdf (true)
Размер: 15.1 MB

An unfortunate outcome of the growth of the internet and mobile technologies has been the challenge of countering cybercrime. This book introduces and explains the latest trends and techniques of Edge Artificial Intelligence (EdgeAI) intended to help cyber security experts design robust Cyber Defense Systems (CDS), including host based and network-based intrusion detection system and digital forensic intelligence.

Big Data and Edge Intelligence for Enhanced Cyber Defense discusses the direct confluence of EdgeAI with Big Data, as well as demonstrating detailed reviews of recent cyber threats and their countermeasures. It provides computational intelligence techniques and automated reasoning models capable of fast training and timely data processing of cyber security Big Data, in addition to other basic information related to network security. In addition, it provides a brief overview of modern cyber security threats and outlines the advantages of using EdgeAI to counter these threats, as well as exploring various Cyber Defense Mechanisms (CDM) based on detection type and approaches. Specific challenging areas pertaining to cyber defense through Edge AI such as improving Digital forensic intelligence, proactive and adaptive defense of network infrastructure and bio-inspired cyber defense mechanisms are also discussed.

This book is intended as a reference for academics and students in the field of network and cybersecurity, particularly on the topics of intrusion detection systems, smart grid, Edge AI and bio-inspired cyber defense principles. The front-line Edge AI techniques discussed will also be of use to cybersecurity engineers in their work enhancing cyber defense systems.

- Explores cutting-edge digital forensic intelligence systems to counter cyber threats.
- Explains modern cyber defense systems using Edge Intelligence techniques Introduces EdgeAI mechanism and its role in cyber defense.
- Provides basic information related to network security Discusses direct confluence of EdgeAI and Big Data.

The book begins by addressing the challenges posed by the evolving threat landscape in Chapter 1, which explores the vulnerabilities in IoT systems and their assessment for sustainable computing. The subsequent chapters navigate through the intricate web of cyber threats and countermeasures, unveiling the power of AI, IoT, and EdgeAI in enhancing cybersecurity. Chapter 2 focuses on the significant threat of phishing and introduces an AI- and IoT-based Intrusion Detection System for Cybersecurity, utilizing the extreme gradient boosting (XGBoost) Algorithm for effective detection. Chapter 3 takes a deep dive into the realm of Digital Forensic Intelligence, highlighting the limitations of traditional approaches and proposing the integration of EdgeAI techniques for real-time analysis, proactive incident response, and improved decision-making. Chapter 4 explores the fusion of artificial intelligence and Blockchain over Edge for Sustainable Smart Cities, shedding light on how these technologies contribute to operational efficiency, environmental sustainability, and the creation of citizen-centric services. Chapter 5 addresses the growing concerns in IoT-based environments, presenting a novel approach using federated learning to enhance intrusion detection. The research emphasizes scalability, privacy, and adaptability to diverse IoT device populations. Chapter 6 tackles the challenge of identifying malicious activities in high-class imbalance networks. The proposed consolidated tree construction (CTC) algorithm showcases remarkable threat detection accuracy, making it a valuable tool in cybersecurity. Chapter 7 concludes the exploration with a comprehensive study of the Internet of Things Intrusion Detection System, examining AI, Deep Learning, and Machine Learning techniques to safeguard interconnected IoT devices and networks.

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