Network Security Empowered by Artificial Intelligence PDF Download

Author: Yingying Chen
Publisher: Springer
ISBN: 9783031535093
Category : Computers
Languages : en
Pages : 0

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Book Description
This book introduces cutting-edge methods on security in spectrum management, mobile networks and next-generation wireless networks in the era of artificial intelligence (AI) and machine learning (ML). This book includes four parts: (a) Architecture Innovations and Security in 5G Networks, (b) Security in Artificial Intelligence-enabled Intrusion Detection Systems. (c) Attack and Defense in Artificial Intelligence-enabled Wireless Systems, (d) Security in Network-enabled Applications. The first part discusses the architectural innovations and security challenges of 5G networks, highlighting novel network structures and strategies to counter vulnerabilities. The second part provides a comprehensive analysis of intrusion detection systems and the pivotal role of AI and machine learning in defense and vulnerability assessment. The third part focuses on wireless systems, where deep learning is explored to enhance wireless communication security. The final part broadens the scope, examining the applications of these emerging technologies in network-enabled fields. The advancement of AI/ML has led to new opportunities for efficient tactical communication and network systems, but also new vulnerabilities. Along this direction, innovative AI-driven solutions, such as game-theoretic frameworks and zero-trust architectures are developed to strengthen defenses against sophisticated cyber threats. Adversarial training methods are adopted to augment this security further. Simultaneously, deep learning techniques are emerging as effective tools for securing wireless communications and improving intrusion detection systems. Additionally, distributed machine learning, exemplified by federated learning, is revolutionizing security model training. Moreover, the integration of AI into network security, especially in cyber-physical systems, demands careful consideration to ensure it aligns with the dynamics of these systems. This book is valuable for academics, researchers, and students in AI/ML, network security, and related fields. It serves as a resource for those in computer networks, AI, ML, and data science, and can be used as a reference or secondary textbook.

Author: Ting Wang
Publisher: Springer Nature
ISBN: 9811973954
Category : Computers
Languages : en
Pages : 123

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Book Description
An Introduction to the Machine Learning Empowered Intelligent Data Center Networking Fundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks. Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security. Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.

Author: Nedunchezhian Raju
Publisher: CRC Press
ISBN: 1000089428
Category : Computers
Languages : en
Pages : 264

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Book Description
This new volume, Empowering Artificial intelligence Through Machine Learning: New Advances and Applications, discusses various new applications of machine learning, a subset of the field of artificial intelligence. Artificial intelligence is considered to be the next-big-game changer in research and technology, The volume looks at how computing has enabled machines to learn, making machine and tools become smarter in many sectors, including science and engineering, healthcare, finance, education, gaming, security, and even agriculture, plus many more areas. Topics include techniques and methods in artificial intelligence for making machines intelligent, machine learning in healthcare, using machine learning for credit card fraud detection, using artificial intelligence in education using gaming and automatization with courses and outcomes mapping, and much more. The book will be valuable to professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology.

Author: Mark Stamp
Publisher: Springer Nature
ISBN: 3030970876
Category : Computers
Languages : en
Pages : 388

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Book Description
This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It’s not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.

Author: StoryBuddiesPlay
Publisher: StoryBuddiesPlay
ISBN:
Category : Computers
Languages : en
Pages : 65

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Book Description
Conquer Cybersecurity Challenges with AI: Your Ultimate Guide In today's ever-connected world, cyber threats loom large. Traditional security methods struggle to keep pace with the sophistication and speed of modern attacks. But there's a new weapon in the fight for digital defense: Artificial Intelligence (AI). This comprehensive guide explores how AI is revolutionizing cybersecurity, empowering businesses and individuals to build a more secure future. Demystifying AI for Cybersecurity: Unveiling the magic behind the curtain, this guide dives into the world of AI and machine learning (ML). We'll explain how AI algorithms analyze vast amounts of data, identify patterns, and predict potential attacks, acting as a vigilant guardian on your digital watchtower. AI's Arsenal of Defensive Tools: From proactive threat detection and prevention to swift incident response and forensics, discover how AI empowers your security team. Explore how AI can identify vulnerabilities before attackers exploit them, analyze network traffic for suspicious activity, and automate the analysis of security alerts, saving valuable time and resources. AI Outmaneuvering Phishing Attacks: Phishing scams remain a persistent threat. This guide unveils how AI thwarts these attempts with deception and advanced detection. Learn about honeypots that lure attackers away from real systems, and AI-powered simulations that train employees to identify phishing tactics. Discover how AI analyzes email content and user behavior to flag suspicious attempts before they cause harm. Understanding User Behavior with UEBA: Imagine a guardian angel monitoring your network for unusual activity. UEBA (User and Entity Behavior Analytics) is just that. This guide explores how AI analyzes user and entity behavior to identify potential insider threats or compromised accounts. Learn how UEBA establishes baselines for normal activity and flags anomalies that might indicate a security breach. Securing the Cloud with AI's Power: The cloud offers flexibility and scalability, but security concerns remain. This guide delves into how AI safeguards cloud-based infrastructure and data. Discover how AI continuously monitors cloud workloads, detects threats in real-time, and automates certain security responses, minimizing the impact of incidents. Beyond the Technology: The Human Element While AI offers immense potential, human expertise remains irreplaceable. This guide emphasizes the importance of collaboration between humans and AI. Explore how security professionals leverage AI insights to make critical decisions and ensure ethical considerations are addressed throughout the cybersecurity process. The Future of AI and Cybersecurity: A Collaborative Journey The cybersecurity landscape is constantly evolving, and AI is at the forefront of this transformation. This guide explores the exciting possibilities and ongoing challenges that lie ahead, including AI's ability to adapt to emerging threats and the potential for automated incident response. Embrace a Secure Future with AI: This guide empowers you to understand AI's role in cybersecurity. By leveraging this powerful technology responsibly, you can build a more robust defense against cyber threats. Take the first step towards a more secure digital future – explore the power of AI in cybersecurity today!

Author: Charles A. Kamhoua
Publisher: John Wiley & Sons
ISBN: 1119723949
Category : Technology & Engineering
Languages : en
Pages : 546

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Book Description
GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.

Author: Velliangiri Sarveshwaran
Publisher: Springer Nature
ISBN: 9819921155
Category : Computers
Languages : en
Pages : 374

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Book Description
This book provides theoretical background and state-of-the-art findings in artificial intelligence and cybersecurity for industry 4.0 and helps in implementing AI-based cybersecurity applications. Machine learning-based security approaches are vulnerable to poison datasets which can be caused by a legitimate defender's misclassification or attackers aiming to evade detection by contaminating the training data set. There also exist gaps between the test environment and the real world. Therefore, it is critical to check the potentials and limitations of AI-based security technologies in terms of metrics such as security, performance, cost, time, and consider how to incorporate them into the real world by addressing the gaps appropriately. This book focuses on state-of-the-art findings from both academia and industry in big data security relevant sciences, technologies, and applications. ​

Author: Haitham M. Alzoubi
Publisher: Springer Nature
ISBN: 3031318013
Category : Computers
Languages : en
Pages : 582

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Book Description
This book takes a unique approach by exploring the connection between cybersecurity, digitalization, and business intelligence. In today's digital landscape, cybersecurity is a crucial aspect of business operations. Meanwhile, organizations continue to leverage digital technologies for their day-to-day operations. They must be aware of the risks associated with cyber-attacks and implement robust cybersecurity measures to protect their assets. It provides practical insights and solutions to help businesses better understand the impact of cybersecurity on their digitalization and business intelligence strategies. It provides practical insights and solutions for implementing cybersecurity measures in organizations and covers a wide range of topics, including threat intelligence, risk management, compliance, cloud security, and IoT security. The book takes a holistic approach and explores the intersection of cybersecurity, digitalization, and business intelligence and examines the possible challenges and opportunities.

Author: Xiaofeng Chen
Publisher: Springer Nature
ISBN: 9813367261
Category : Computers
Languages : en
Pages : 168

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Book Description
Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

Author: Meng Shen
Publisher: Springer Nature
ISBN: 9811559392
Category : Computers
Languages : en
Pages : 135

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Book Description
With the development of big data, data sharing has become increasingly popular and important in optimizing resource allocation and improving information utilization. However, the expansion of data sharing means there is an urgent need to address the issue of the privacy protection – an area where the emerging blockchain technology offers considerable advantages. Although there are a large number of research papers on data sharing modeling and analysis of network security, there are few books dedicated to blockchain-based secure data sharing. Filing this gap in the literature, the book proposes a new data-sharing model based on the blockchain system, which is being increasingly used in medical and credit reporting contexts. It describes in detail various aspects of the model, including its role, transaction structure design, secure multi-party computing and homomorphic encryption services, and incentive mechanisms, and presents corresponding case studies. The book explains the security architecture model and the practice of building data sharing from the blockchain infrastructure, allowing readers to understand the importance of data sharing security based on the blockchain framework, as well as the threats to security and privacy. Further, by presenting specific data sharing case studies, it offers insights into solving data security sharing problems in more practical fields. The book is intended for readers with a basic understanding of the blockchain infrastructure, consensus mechanisms, smart contracts, secure multiparty computing, homomorphic encryption and image retrieval technologies.