As AI technologies progress and influence more facets of our lives, the requirement for openness and interpretability becomes increasingly important. Explainable AI (XAI) has the potential to be a paradigm shift in the next generation of AI systems. XAI strives to make AI algorithms and methods understandable by tackling trust, bias, compliance, and accountability challenges. XAI improves model disclosure, produces intrinsically interpretable deep learning approaches, offers real-time rationales, and promotes legitimate AI practice. These advances assist in the development of a more ethically sound AI ecosystem.
As the IoT evolves and supply chains become more complex, novel avenues for attack arise. The ever-changing threat landscape includes powerful adversaries such as malicious actors and hackers who are always refining their strategies, and demand ongoing monitoring and adaptive responses. Cybersecurity helps safeguard data, identify fraud, protect vital infrastructure, and ensure confidentiality. Considering the dynamic nature of the cybersecurity battlefront, a holistic approach must include pre-emptive threat intelligence, staff training, effective security tools, regular upgrades, and global collaboration. Explainable AI (XAI) explains security alerts, reduces false positives and enables faster incident response.
The objective of this book is to explore how the integration of XAI-based cybersecurity algorithms and methods support threat detection and decision-making by preserving privacy and trust, ensuring interpretability and accountability, and optimizing computational and communication costs.
This book will be a useful reference for computing and security researchers, scientists, and IT professionals in academia and industry, who are developing and designing innovative cyber threat and vulnerability detection systems and solutions, as well as advanced students and lecturers to better understand AI and XAI algorithms for cybersecurity applications.
Chapter 1: The Present, Past, and Future Aspects of XAI in
Cybersecurity
Chapter 2: Bridging the Gap: Explainable AI in Threat Detection and
Cybersecurity
Chapter 3: Explainable Artificial Intelligence in Threat Detection
Chapter 4: XAI enabled Blockchain for Cybersecurity
Chapter 5: A Hybrid Cybersecurity Approach: Leveraging ZTA, AI, XAI, and
Blockchain to Combat Emerging Threats
Chapter 6: Deep Reinforcement Learning for Cybersecurity
Chapter 7: Trustworthy Explainable Artificial Intelligence for Resilient
Cybersecurity Applications
Chapter 8: Malware Analysis in IoT Devices and AI
Chapter 9: A multimodal framework for intrusion detection in IoT: integrating
transfer learning, game theory, and Nash equilibrium
Chapter 10: Cyber Security for Internet of Things: Big Data Optimization for
IoT-Based Real-Time Network Traffic Analysis
Chapter 11: Advancing VANET Resilience: Integrating Ensemble Learning with
Large Language Models to Combat Fake Report Attacks
Chapter 12: Cybersecurity-enabled Federated Learning Approach for Digital
Healthcare
Chapter 13: IoT Guardian: Explainable Deep Ensemble Learning for Reliable
Security in Internet of Medical Things
Chapter 14: Intelligence for Next Generation: Social and Ethical Challenges
Chapter 15: The Ethics of Artificial Intelligence: Issues and Prospects for
Future Generations
Farhan Ullah is an associate research professor at the Cybersecurity Center of Prince Mohammad Bin Fahd University, Saudi Arabia. His areas of research interest include malware analysis, data science, AI and cybersecurity. He works as an editorial board member for several SCIE journals including KSII Transactions on Internet and Information Systems, Electronics, CMC Computers, Materials & Continua, and CSSE Computer Systems Science and Engineering. He has published 58 papers in peer reviewed journals.
Gautam Srivastava is a full professor in the Department of Mathematics and Computer Science at Brandon University, Canada. He is active in the research fields of AI, cybersecurity, data mining, and big data. He has extensive Guest Editorial Experience including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Industrial Informatics, Computer Standards and Interfaces and Applied Stochastic Modeling and Business. He has published 400 papers in high-impact conferences and journals.
Awais Ahmad is an assistant professor in the Department of Information Systems, College of Computer and Information Sciences at Imam Muhammad Ibn Saud Islamic University, Riyadh, Kingdom of Saudi Arabia. His research interests include cybersecurity, deep learning, machine learning, artificial intelligence, denoising and demosaicking, big data analytics, and internet of things. He has published 170+ International papers in journals including IEEE Transactions, IEEE Magazines and ACM Transactions.
