Malware Analysis Using Artificial Intelligence and Deep Learning

1. Optimizing Multi-class Classification of Binaries Based on Static Features.- 2.Detecting Abusive Comments Using Ensemble Deep Learning Algorithms.- 3. Deep Learning Techniques for Behavioural Malware Analysis in Cloud IaaS.- 4. Addressing Malware Attacks on Connected and Autonomous Vehicles: Recent Techniques and Challenges.- 5. A Selective Survey of Deep Learning Techniques and Their Application to Malware Analysis.- 6. A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification.- 7. Word Embedding Techniques for Malware Evolution Detection.- 8. Reanimating Historic Malware Samples.- 9. DURLD: Malicious URL detection using Deep learning based Character-level representations.- 10. Sentiment Analysis for Troll Detection on Weibo.- 11. Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families.- 12. Review of the Malware Categorization in the Era of Changing Cybethreats Landscape: Common Approaches, Challenges and Future Needs.- 13. An Empirical Analysis of Image-Based Learning Techniques for Malware Classification.- 14. A Survey of Intelligent Techniques for Android Malware Detection.- 15. Malware Detection with Sequence-Based Machine Learning and Deep Learning.- 16. A Novel Study on Multinomial Classification of x86/x64 Linux ELF Malware Types and Families through Deep Neural Networks.- 17. Cluster Analysis of Malware Family Relationships.- 18. Log-Based Malicious Activity Detection using Machine and Deep Learning.- 19. Deep Learning in Malware Identification and Classification.- 20. Image Spam Classification with Deep Neural Networks.- 21. Fast and Straightforward Feature Selection Method.- 22. On Ensemble Learning.- 23. A Comparative Study of Adversarial Attacks to Malware Detectors Based on Deep Learning.- 24. Review of Artificial Intelligence Cyber Threat Assessment Techniques for Increased System

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 digital rights management product for a Silicon Valley start-up company. Since 2002, Dr. Stamp has been 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 140 research papers, most of which deal with problems at the interface between machine learning and information security. Dr. Stamp served as co-editor of the Handbook of Information and Communication Security (Springer, 2010), and he is the author of four books, including a popular information security textbook(Information Security: Principles and Practice, 2nd edition, Wiley, 2011) and, most recently, a machine learning textbook (Introduction to Machine Learning with Applications in Information Security, Chapman and Hall/CRC, 2017).

Mamoun Alazab received his PhD degree in Computer Science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is currently an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. He is a cyber-security researcher and practitioner with industry and academic experience. Dr. Alazab's research is multidisciplinary, with a focus on cyber security and digital forensics of computer systems, including current and emerging issues in the cyber environment, such as cyber-physical systems and the Internet of Things. His research takes into consideration the unique challenges present in these environments, with an emphasis on cybercrime detection and