Malware Analysis Using Artificial Intelligence and Deep Learning

June 14, 2021
Malware Analysis Using Artificial Intelligence and Deep Learning

This book is focused on the use of deep learning (DL) andartificial intelligence (AI) as tools to advance the fields ofmalware detection and analysis. The individual chapters of the bookdeal with a wide variety of state-of-the-art AI and DL techniques,which are applied to a number of challenging malware-relatedproblems. DL and AI based approaches to malware detection andanalysis are largely data driven and hence minimal expert domainknowledge of malware is needed.This book fills a gap between the emerging fields of DL/AI andmalware analysis. It covers a broad range of modern and practicalDL and AI techniques, including frameworks and development toolsenabling the audience to innovate with cutting-edge researchadvancements in a multitude of malware (and closely related) usecases.About the AuthorMark Stamp has extensive experience in information security andmachine learning, having worked in these fields within academic,industrial, and government environments. After completing his PhDresearch in cryptography at Texas Tech University, he spent morethan seven years as a cryptanalyst with the United States NationalSecurity Agency (NSA), followed by two years developing a digitalrights management product for a Silicon Valley start-up company.Since 2002, Dr. Stamp has been a Professor in the Department ofComputer Science at San Jose State University, where he teachescourses in machine learning and information security. To date, hehas published more than 140 research papers, most of which dealwith problems at the interface between machine learning andinformation security. Dr. Stamp served as co-editor of the Handbookof Information and Communication Security (Springer, 2010), and heis the author of four books, including a popular informationsecurity textbook (Information Security: Principles and Practice,2nd edition, Wiley, 2011) and, most recently, a machine learningtextbook (Introduction to Machine Learning with Applications inInformation Security, Chapman and Hall/CRC, 2017).Mamoun Alazab received his PhD degree in Computer Science fromthe Federation University of Australia, School of Science,Information Technology and Engineering. He is currently anAssociate Professor in the College of Engineering, IT andEnvironment at Charles Darwin University, Australia. He is acyber-security researcher and practitioner with industry andacademic experience. Dr. Alazab's research is multidisciplinary,with a focus on cyber security and digital forensics of computersystems, including current and emerging issues in the cyberenvironment, such as cyber-physical systems and the Internet ofThings. His research takes into consideration the unique challengespresent in these environments, with an emphasis on cybercrimedetection and prevention. He has a particular interest in theapplication of machine learning as an essential tool forcybersecurity, examples of which include detecting attacks,analyzing malicious code, and uncovering vulnerabilities insoftware. He is the Founder and the Chair of the IEEE NorthernTerritory Subsection (February 2019 - present), a Senior Member ofthe IEEE, Cybersecurity Academic Ambassador for Oman's InformationTechnology Authority (ITA), and Member of the IEEE ComputerSociety's Technical Committee on Security and Privacy (TCSP). Inaddition, he has collaborated with government and industry on manyprojects, including work with IBM, Trend Micro, Westpac, theAustralian Federal Police (AFP), the Australian Communications andMedia Authority (ACMA), Westpac, UNODC to name a few.Andrii Shalaginov is a Researcher in Information Security andDigital Forensics at the Department of Information Security andCommunication Technology, Faculty of Information Technology andElectrical Engineering, Norwegian University of Science andTechnology (NTNU). Dr. Shalaginov was awarded the PhD degree inInformation Security from NTNU in February 2018. During the lastdecade, Dr. Shalaginov's focus has been on the fields of cybercrimeinvestigation and intelligent malware detection. His primaryexpertise is in static and dynamic malware analysis, development ofmachine learning-aided intelligent computer virus detection models,and similarity-based categorization of cyberattacks in the Internetof Things. Further, Dr. Shalaginov has worked as a securityresearcher for UNICRI/EUIPO on malware analysis forcopyright-infringing websites. He was nominated as a representativefrom Norway at COST Action CA17124 "DigForAsp - Digital forensics:evidence analysis via intelligent systems and practices". In 2018,Dr. Shalaginov, together with his NTNU team, received an award forfirst place in the "Future of Smart Policing" hackathon competitionsponsored by INTERPOL (Singapore). Dr. Shalaginov also holds asecond Master's Degree in Information Security (Digital Forensics)from Gjøvik University College (GUC), and he received BSc and MScdegrees in System Designing from the National Technical Universityof Ukraine "Kyiv Polytechnic Institute", Department ofComputer-Aided Design. Finally, Dr. Shalaginov is LE-1/LPIC-1certified and has extensive industry experience, including work atSamsung R&D Center.