A Deep Learning Framework to preserve Privacy in Federated (Collaborative) Learning -- Biometric Fingerprint Generation using Generative Adversarial Networks -- Assessing Cybersecurity Economic Risks in Virtual Power Plant UsingDeep Learning Techniques -- Biometric E-Voting System for Cybersecurity -- Proactive Network Packet Classification using Artificial Intelligence -- Security and Information Assurance for IoT-based Big Data -- Privacy Preservation Approaches for Social Network Data Publishing.

This book provides stepwise discussion, exhaustive literature review, detailed analysis and discussion, rigorous experimentation results (using several analytics tools), and an application-oriented approach that can be demonstrated with respect to data analytics using artificial intelligence to make systems stronger (i.e., impossible to breach). We can see many serious cyber breaches on Government databases or public profiles at online social networking in the recent decade. Today artificial intelligence or machine learning is redefining every aspect of cyber security. From improving organizations' ability to anticipate and thwart breaches, protecting the proliferating number of threat surfaces with Zero Trust Security frameworks to making passwords obsolete, AI and machine learning are essential to securing the perimeters of any business. The book is useful for researchers, academics, industry players, data engineers, data scientists, governmental organizations, and non-governmental organizations.