Artificial Intelligence for 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.

Mark Stamp has extensive experience in information security and machine learning, having worked in these fields within academic, industrial, and government environments. After completing his PhD research in cryptography at Texas Tech University, he spent more than seven years as a cryptanalyst with the United States National Security Agency (NSA), followed by two years developing a security product for a Silicon Valley start-up company. Since early in the present century, Dr. Stamp has been employed as a Professor in the Department of Computer Science at San Jose State University, where he teaches courses in machine learning and information security. To date, he has published more than 150 research articles, most of which deal with problems at the interface between machine learning and information security. Dr. Stamp served as a co-editor of the Handbook of Information and Communication Security (Springer, 2010) and Malware Analysis using Artificial Intelligence and Deep Learning (Springer 2020), and he is the author of multiple textbooks, including Information Security: Principles and Practice (Wiley, 3rd edition, 2021) and Introduction to Machine Learning with Applications in Information Security (Chapman and Hall/CRC, 2nd edition, 2022).


Corrado Aaron Visaggio is an associate professor at the Department of Engineering of the University of Sannio, where he teaches "Security of Networks and Software Systems" at the MSc in Computer Engineering. Currently he is also Chief Scientist Officer at Defence Tech, a company operating in Cybersecurity, Aerospace and Military Engineering. He obtained the MSc in Electronic Engineering (2001) from Politecnico di Bari, and the PhD in Information Engineering (2005) from University of Sannio. His main research interests are: malware analysis, data protection, data protection, threat intelligence. He teaches in Master Programs of Cybersecurity of University of