OWL Ontologies in Cybersecurity: Conceptual Modeling of Cyber-Knowledge -- Knowledge Representation of Network Semantics for Reasoning-Powered Cyber-Situational Awareness -- The Security of Machine Learning Systems -- Patch Before Exploited: An Approach to Identify Targeted Software Vulnerabilities -- Applying Artificial Intelligence Methods to Network Attack Detection -- Machine Learning Algorithms for Network Intrusion Detection -- Android Application Analysis using Machine Learning Techniques.

This book presents a collection of state-of-the-art AI approaches to cybersecurity and cyberthreat intelligence, offering strategic defense mechanisms for malware, addressing cybercrime, and assessing vulnerabilities to yield proactive rather than reactive countermeasures. The current variety and scope of cybersecurity threats far exceed the capabilities of even the most skilled security professionals. In addition, analyzing yesterday's security incidents no longer enables experts to predict and prevent tomorrow's attacks, which necessitates approaches that go far beyond identifying known threats. Nevertheless, there are promising avenues: complex behavior matching can isolate threats based on the actions taken, while machine learning can help detect anomalies, prevent malware infections, discover signs of illicit activities, and protect assets from hackers. In turn, knowledge representation enables automated reasoning over network data, helping achieve cybersituational awareness. Bringing together contributions by high-caliber experts, this book suggests new research directions in this critical and rapidly growing field.