Introduction -- By Failing to Prepare - You are preparing to Fail -- Authentication Methods and Applications -- Design of Blockchain Polynomial Digests to Secure Method Authentication -- Need of Cybersecurity for ERP Systems -- IoT - Architecture Vulnerabilities and Security Measures -- Cybersecurity Advanced Persistent Threat Attacks -- Securing Deep Learning Neural Networks -- Three Tier Architecture to Mitigate DDoS attacks on Hybrid Cloud Environments -- Conclusion.

This book provides use case scenarios of machine learning, artificial intelligence, and real-time domains to supplement cyber security operations and proactively predict attacks and preempt cyber incidents. The authors discuss cybersecurity incident planning, starting from a draft response plan, to assigning responsibilities, to use of external experts, to equipping organization teams to address incidents, to preparing communication strategy and cyber insurance. They also discuss classifications and methods to detect cybersecurity incidents, how to organize the incident response team, how to conduct situational awareness, how to contain and eradicate incidents, and how to cleanup and recover. The book shares real-world experiences and knowledge from authors from academia and industry. Shares cases studies on using ML and AI to predict and preempt cyber attacks; Describes security attacks, trends, and scenarios along with attack vectors for various domains and industry sectors; Includes detail on incident planning, detection methods, containing incidents, and clean up and recovery.