Explainable Artificial Intelligence (XAI) for Next Generation Cybersecurity

Farhan Ullah is associate research professor at the Cybersecurity Center of Prince Mohammad Bin Fahd University, Saudi Arabia. He has previously held positions as associate professor at Northwestern Polytechnical University, China, visiting research fellow at the University of Camerino, Italy, and lecturer at COMSATS University Islamabad, Pakistan. He has received various awards for his research and teaching excellence. He has also been invited as a keynote speaker and has delivered courses at international conferences and summer schools. He had the privilege of contributing as a guest editor and engaging in editorial work for renowned journals such as the IEEE Journal of Biomedical and Health Informatics and KSII Transactions, among others. He has supervised several undergraduate and graduate students and served as a foreign thesis examiner for PhD theses. His work has been published in reputable journals, including those published by IEEE, Springer, Elsevier, and Wiley. His primary research areas include cybersecurity, malware analysis, data science, deep learning, and explainable AI. He received his PhD in computer science from Sichuan University, China.Gautam Srivastava is 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. His research is funded by NSERC and MITACS, and he sits on the Discovery Grant Evaluation Group for Computer Science for NSERC. He currently has active research projects with other academics in Taiwan, Singapore, Canada, and the USA. He is a senior member of the IEEE. He is an editor for top tiered journals including Information Sciences, IEEE TII, IEEE TCSS, IEEE IoT Journal, and Expert Systems. He holds a PhD degree in computer science from the University of Victoria British Columbia, Canada.Awais Ahmad is assistant professor in the Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia. He was previously a researcher in INTEL-NTU, National Taiwan University, Taiwan, where he was working on the Wukong Project (Smart Home). 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, and at conference such as IEEE GLOBECOM, IEEE INFOCOM, IEEE LCN, and IEEE ICC. He is serving as a guest editor for several journals including Future Generation Computer Systems, Sustainable City and Societies, Computational Intelligence and Complexity, Multimedia Tools and Applications, IEEE Access, and Real-Time Image Processing Journal (Springer). He received his PhD degree in computer science and engineering from Kyungpook National University, Daegu, Korea.

• Chapter 1: The present, past, and future aspects of XAI in cybersecurityChapter 2: Bridging the gap: explainable AI in threat detection and cybersecurityChapter 3: Explainable artificial intelligence in threat detectionChapter 4: XAI-enabled blockchain for cybersecurityChapter 5: A hybrid cybersecurity approach: leveraging ZTA, AI, XAI, and blockchain to combat emerging threatsChapter 6: Deep reinforcement learning for cybersecurityChapter 7: Trustworthy explainable artificial intelligence for resilient cybersecurity applicationsChapter 8: Malware analysis in IoT devices and AIChapter 9: A multimodal framework for intrusion detection in IoT: integrating transfer learning, game theory, and Nash equilibriumChapter 10: Cybersecurity for Internet of Things: big data optimization for IoT-based real-time network traffic analysisChapter 11: Advancing VANET resilience: integrating ensemble learning with large language models to combat fake report attacksChapter 12: Cybersecurity-enabled federated learning approach for digital healthcareChapter 13: IoT guardian: explainable deep ensemble learning for reliable security in Internet of Medical ThingsChapter 14: Artificial intelligence for next generation: social and ethical challengesChapter 15: The ethics of artificial intelligence: issues and prospects for future generationsConclusion