This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity.

Part I: Malware-Related Topics.- Generation of Adversarial Malware and Benign Examples using Reinforcement Learning.- Auxiliary-Classifier GAN for Malware Analysis.- Assessing the Robustness of an Image-based Malware Classifier with Small Level Perturbations Techniques.- Detecting Botnets Through Deep Learning and Network Flow Analysis.- Interpretability of Machine Learning-Based Results of Malware Detection Using a Set of Rules.- Mobile Malware Detection using Consortium Blockchain.- BERT for Malware Classification.- Machine Learning for Malware Evolution Detection.- Part II: Other Security Topics.- Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-based Side-channel Analysis.- Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication.- Clickbait Detection for YouTube Videos.- Survivability Using Artificial Intelligence Assisted Cyber Risk Warning.- Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics.- Machine Learning-Based Analysis of Free-Text Keystroke Dynamic.- Free-Text Keystroke Dynamics for User Authentication.