دانلود کتاب هوش مصنوعی قابل توضیح برای نسل بعدی مفاهیم امنیت سایبری

Explainable AI (XAI) has the potential to be a paradigm shift in the next generation of artificial intelligence (AI) systems. As AI technologies progress and influence more facets of our lives, the requirement for openness and interpretability becomes increasingly important. XAI strives to make AI algorithms and methods for decision-making understandable to people, tackling trust, justice, and accountability challenges. It helps people comprehend why AI systems make certain decisions, reduce biases, and make it easier to comply with rules. XAI is predicted to emerge in the future AI era by improving model disclosure, producing intrinsically interpretable deep learning approaches, offering real-time rationales and promoting legitimate AI practice. These advances in explainability are critical for developing trust, enabling interaction between humans and AI, and assuring sustainable and legal AI deployment across various industries. They not only enable users to make intelligent choices based on AI recommendations, but they also support continuing study into AI’s legal and open use, assisting in the development of a more ethically sound AI ecosystem in the future. In the dynamic domain of cybersecurity, a multitude of complex challenges persist, necessitating constant surveillance and advancement. Cybersecurity apps safeguard data, identify fraud, protect vital infrastructure, and assure confidentiality for businesses ranging from banking to healthcare to the state. They also protect transactions, Internet of Things (IoT) devices, and information. The protection of assets and information is crucial in the increasingly digital world. The ever-changing threat landscape includes powerful adversaries such as malicious actors and hackers funded by states who are always refining their strategies. The advent of zero-day exploits, as well as the disrupting surge of ransomware attacks, emphasizes the critical nature of the problem. As the IoT evolves and supply chains become more complicated, novel avenues for attack arise, challenging defense measures. The persistent susceptibility of the human component, as demonstrated by successful phishing attempts, emphasizes the importance of continued education and awareness efforts. Advanced persistent threats, which are frequently organized by nationstates, demand ongoing monitoring and adaptive responses. Compliance with severe data privacy standards, such as General Data Protection Regulation and California Consumer Privacy Act, adds to the complexity of handling data ecosystems. These issues are exacerbated by insider risks, cloud security, and the global nature of cyberattacks. Considering the dynamic nature of the cybersecurity battlefront, a holistic approach must include preemptive threat intelligence, staff training, effective security tools, regular upgrades, and global collaboration. Incorporating XAI into cybersecurity increases threat detection and decision-making. XAI explains security alerts, reducing false positives and enabling faster incident response. Transparency and accountability for AIdriven security practices help with compliance, user awareness, and trustbuilding. XAI learns from new data to optimize resource utilization and adapt to emerging threats, making it effective in the modern complex cybersecurity landscape. The objective of this book is to provide insight on the applications of XAI to solve some of the issues of data processing and vulnerabilities in cybersecurity applications. This collection of information also provides a detailed discussion on how XAI-based cybersecurity algorithms can be used to handle dynamic nature of cyberattacks, preserve privacy, optimize computational and communication costs, etc. The chapters provide both practical and theoretical knowledge for global researchers and practitioners who are working in the fields of XAI, cybersecurity applications, and machine and deep learning. Finally, this book is meant to give useful insights and act as a reference book for advanced students and researchers in academia and industry. The chapters in this book include XAI in cybersecurity applications such as malware analysis, trustworthy XAI, IoT, healthcare, big data, large language models, vehicular networks, federated learning, blockchain, reinforcement learning, and threat detection. Moreover, some emphasis is given in the book on the ethical and social challenges that exist in the next generation of AI. Chapter 1 gives an overview of XAI in cybersecurity looking at it from the lens of past, present, and future, while Chapter 2 bridges the gap with XAI in threat detection. Chapter 3 looks into XAI in threat detection. Chapter 4 integrates XAI with blockchain to tackle cybersecurity issues. Chapter 5 leverages blockchain and AI to combat issues in threat mitigation. Chapter 6 describes deep reinforcement learning for cybersecurity, while Chapter 7 summarizes trustworthy XAI. Chapter 8 investigates malware analysis in the IoT. Chapter 9 uses game theory and AI for tackling threat detection in IoT. Chapter 10 tackles security issues in network traffic in IoT, while Chapter 11 looks at vehicular communication in vehicular ad hoc networks and how large language models can be used to ensure reliable networks. Shifting focus to learning systems, Chapter 12 looks at using a federated learning system in digital healthcare. Focusing on IoT, Chapter 13 gives an overview of IoT guardian meant to mitigate reliability issues in the Internet of Medical Things. Chapters 14 and 15 dive into the ethical and social challenges that exist in the next generation of AI. The editors would like to thank Olivia Wilkins, Brittany Insull, and Valerie Moliere as well as the rest of the IET staff for their editorial assistance and support in producing this important scientific work. Without this collective effort, this book would not have been possible to be completed.