Machine Learning, Deep Learning and AI for Cybersecurity

Mark Stamp has been active in the field of information security for more than three decades. Following his Ph.D. research in cryptography, he spent the better part of a decade as a cryptanalyst with the US National Security Agency (NSA), followed by two years developing a cybersecurity product for a Silicon Valley startup company. For the past 20 years, Dr. Stamp has been a faculty member in the Department of Computer Science at San Jose State University, where he has developed and regularly teaches courses in information security and machine learning. He has published more than 175 research articles, most of which are at the interface between information security and machine learning. Dr. Stamp has served as a co-editor for several books, including the Handbook of Information and Communication Security (Springer, 2010) and Malware Analysis Using Artificial Intelligence and Deep Learning (Springer, 2021). 

Martin Jurecek is an assistant professor in computer science at Czech Technical University in Prague. He graduated from the Charles University in Prague, Faculty of Mathematics and Physics, with a specialization in mathematical methods of information security. He received his Ph.D. at the Czech Technical University in Prague, Faculty of Information Technology, specializing in automatic malware detection. Dr. Jurecek worked for five years as a malware researcher in the antivirus industry and two years as a data scientist on several projects in the field of information security for the banking and telecommunications sectors. He has been working as an assistant professor at the Czech Technical University in Prague for over ten years. His main research interests focus on the application of machine learning and artificial intelligence approaches to malware detection. Other areas of his interest are algebraic cryptanalysis and the security of cryptocurrencies.