Cyber Resilience

Swati SahDr. Swati Sah is a Professor at Sharda University, India, with more than 12 years of experience in higher education, research, academic leadership, and international collaborations. She has built a distinguished academic career through her contributions to teaching, research, curriculum development, and institutional administration.Prior to joining Sharda University, Dr. Sah served as a faculty member at Amity University, Uzbekistan. In May 2018, she was appointed as the Head of the Department of Computer Science at Patan College for Professional Studies (PCPS), Nepal, an institution affiliated with the University of Bedfordshire, United Kingdom. During her tenure, she played a pivotal role in strengthening academic programs, promoting research activities, and fostering industry–academia collaboration.Dr. Sah holds a Master of Computer Applications (MCA) from Dr. A.P.J. Abdul Kalam Technical University (formerly Uttar Pradesh Technical University), Lucknow, India, and an M.Sc. from Birmingham City University, United Kingdom. Throughout her academic journey, she has remained actively involved in professional societies, research collaborations, curriculum design, mentoring undergraduate and postgraduate students, and supervising research projects.Her research interests encompass Artificial Intelligence (AI), Machine Learning (ML), Cybersecurity, Data Analytics, Intelligent Systems, and AI-driven Decision Support Systems. She has authored and edited scholarly books, published research articles in reputed journals and conference proceedings, and presented her work at numerous national and international conferences. She also serves as a reviewer, editor, and external examiner for academic journals, conferences, and doctoral research.Dr. Sah's current research focuses on developing intelligent cybersecurity frameworks that leverage Artificial Intelligence and Machine Learning for cyber threat detection, risk assessment, anomaly detection, and automated incident response. She is equally passionate about interdisciplinary research that applies emerging technologies to healthcare, education, agriculture, and smart digital ecosystems.A strong advocate of innovation and lifelong learning, Dr. Sah actively mentors students and researchers, encourages collaborative research, and promotes the responsible and ethical adoption of Artificial Intelligence. Her vision is to bridge the gap between cutting-edge research and practical applications by developing intelligent, secure, and sustainable technology solutions that create meaningful societal impact.Research InterestsArtificial IntelligenceMachine LearningCybersecurityData AnalyticsIntelligent Decision Support SystemsExplainable AI (XAI)Deep LearningIntelligent Healthcare SystemsSmart Digital EcosystemsProfessional InterestsResearch and InnovationInternational Academic CollaborationCurriculum DevelopmentDoctoral Research SupervisionAI for Sustainable DevelopmentIndustry–Academia CollaborationEthical and Responsible AIAditya Dayal TyagiAditya Dayal Tyagi is a passionate academician, mentor, and researcher. He is specialist in Data Structure, Algorithm Design and Analysis, and Data Base Technologies. He has 20+ years of teaching experience in MCA/B.Tech (CSE/IT) students. He completed his MCA from Dewan Institute of Management Technology Meerut, Uttar Pradesh Technical University (UPTU) – Lucknow and earned his M. TECH in Computer Science and Engineering from GBTU-Lucknow (Previously known as Uttar Pradesh Technical University (UPTU) – Lucknow). He is pursuing his Ph.D from JayPee Institute of Information Technology (Deemed to be University), Noida. His research areas are Information Diffusion, Social Network Analysis, Sentiments Analysis, Signcyption Approach, Deep Learning, and algorithm analysis. He is authored of a book titled "AI-Powered Pricing: Transforming Business with Intelligent Pricing Models." He has one patent and published and presented many research papers in International Journals, International Conferences and National Conferences of reputeRejwan Bin SulaimanDr. Rejwan Bin Sulaiman is currently serving as a Lecturer in Cyber Security at the University of Law, United Kingdom. He earned his Ph.D. in Artificial Intelligence and Cybersecurity from the University of Bedfordshire, where his doctoral research focused on federated learning-based approaches for secure and privacy-preserving financial AI systems. He has held academic positions at several institutions, including Northumbria University and Arden University.Dr. Sulaiman’s research spans Cybersecurity, Artificial Intelligence, Computer Vision, and Machine Learning, with particular interest in developing decentralized AI models that enhance data security and user privacy. His work has been published in leading venues such as IEEE, Springer, and CRC Press, contributing to the advancement of secure machine learning frameworks in distributed environments.He is a Certified Ethical Hacker (CEH) and the founder of STEMResearch.Ai, an initiative that supports and mentors early-career researchers in STEM fields. He is also a Fellow of the Higher Education Academy (FHEA) and has received multiple awards recognizing his innovative teaching practices and dedication to academic excellence.Pranjali GajbhiyeDr. Pranjali Gajbhiye is currently working as a Neuroscientist at Woxsen University, Hyderabad, India, with a strong academic and industry background in biomedical signal processing. With two years of hands-on experience in the BCI-based healthcare industry, she specializes in EEG and fNIRS signal processing using wearable sensing technologies. She holds a PhD in Biomedical Signal Processing (ECE) from BITS Pilani, an M.Tech in Communication System Engineering from VNIT Nagpur, and a B.Tech in ECE from Nagpur University. Her research interests include biomedical signal processing, image and video processing, medical image analysis, and IoT applications in healthcare. She has authored 12+ research articles, contributed to multiple book chapters, and presented at numerous international conferences. She holds 5 patents (published), and serves as a book editor. Her work is published in Q1 journals, including IEEE Transactions on Instrumentation and Measurement. She also contributes academically as a Review Editor for the Q1 journal Frontiers in Physiology (Impact Factor 4.13)

• Chapter 1: Introduction: The Convergence of Federated Learning, BCI, and Cyber Resilience. Chapter 2: Federated Learning Foundations for Secure and Private AI. Chapter 3: Cyber Threat Models and Attack Surfaces in Federated Learning System. Chapter 4: Brain-Computer Interfaces (BCIs): Opportunities and Security Challenges. Chapter 5: Neuro-Adaptive Defense Mechanisms for Federated Learning. Chapter 6: Privacy-Preserving Learning with Cognitive Feedback Loops. Chapter 7: Resilient Architectures for Federated Learning: Design Patterns and Recovery Models. Chapter 8: Blockchain and Distributed Ledger Technologies in Resilient FL System. Chapter 9: Energy-Conscious and Secure Edge Devices for Brain-AI Integration. Chapter 10: Explainable AI in Neuro-Federated Learning Environments. Chapter 11: Case Study: Cyber-Resilient Neuro-AI in Healthcare Systems. Chapter 12: Case Study: Federated Brain-AI Systems in Smart Industry Applications. Chapter 13: Case Study: Federated Brain-AI Systems in Smart Industry Applications. Chapter 14: Legal, Ethical, and Regulatory Challenges in Neuro-Federated AI. Chapter 15: Simulating Attacks and Recovery in Federated Neuro-AI Systems. Chapter 16: Future of Secure Neuro-Symbolic Federated Intelligence. Chapter 17: Conclusion: Building Secure, Resilient.