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 Jureček 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. Jureček 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.

• Online Clustering of Known and Emerging Malware Families.- Applying Word Embeddings and Graph Neural Networks for Effective Malware Classification.- A Comparative Analysis of SHAP and LIME in Detecting Malicious URLs.- Comparing Balancing Techniques for Malware Classification.- Multimodal Deception and Lie Detection Using Linguistic and Acoustic Features, Deep Models, and Large Language Models.- Enhancing Dynamic Keystroke Authentication with GAN-Optimized Deep Learning Classifiers.- Selecting Representative Samples from Malware Datasets.- FLChain: Integration of Federated Learning and Blockchain for Building Unified Models for Privacy Preservation.- On the Steganographic Capacity of Selected Learning Models.- An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack.- An Empirical Analysis of Hidden Markov Models with Momentum.- Image-Based Malware Classification Using QR and Aztec Codes.- Keystroke Dynamics for User Identification.- Distinguishing Chatbot from Human.- Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network.- Temporal Analysis of Adversarial Attacks in Federated Learning.- Steganographic Capacity of Transformer Models.- Robustness of Selected Learning Models under Label Flipping Attacks.- Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks.- Quantum Computing Methods for Malware Detection.- Reducing the Surface for Adversarial Attacks in Malware Detectors.- XAI and Android Malware Models.