Overview
- Provides guidance on how to explore the cutting-edge AI/ML applications in digital forensics
- Reveals the significant impact AI/ML on strengthening the security and privacy of cyber physical systems
- Analyzes massive amounts of data in real-time and identify complex threat patterns
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Table of contents (12 chapters)
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Front Matter
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AI/ML for Cyber Analysis
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Front Matter
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Keywords
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Federated Learning
- Information Assurance
- Cyber Security
- Digital Forensics
- Cyber Physics Systems
- Cyber Analysis
- Generative Adversarial Network
- Graph Neural Networks
- Gated Recurrent Unit
- Intrusion Detection System
- Internet of Things
- Natural Language Processing
About this book
The rapid growth and reliance on cyber systems have permeated our society, government, and military which is demonstrated in this book. The authors discuss how AI-powered cyber systems are designed to protect against cyber threats and ensure the security and reliability of digital systems using artificial intelligence (AI) technologies. As AI becomes more integrated into various aspects of our lives, the need for reliable and trustworthy AI systems becomes increasingly important. This book is an introduction to all of the above-mentioned areas in the context of AI Embedded Assurance for Cyber Systems.
This book has three themes. First, the AI/ML for digital forensics theme focuses on developing AI and ML powered forensic tools, techniques, software, and hardware. Second, the AI/ML for cyber physical system theme describes that AI/ML plays an enabling role to boost the development of cyber physical systems (CPS), especially in strengthening the security and privacy ofCPS. Third, the AI/ML for cyber analysis theme focuses on using AI/ML to analyze tons of data in a timely manner and identify many complex threat patterns.
This book is designed for undergraduates, graduate students in computer science and researchers in an interdisciplinary area of cyber forensics and AI embedded security applications. It is also useful for practitioners who would like to adopt AIs to solve cyber security problems.
Editors and Affiliations
About the editors
His path-breaking discovery known as Brooks-Iyengar algorithm discovered in 1996 is a milestone in his career. This discovery has led to a breakthrough in the use of sensors in various applications across the globe. By adopting this algorithm, it was possible to use a network of sensors that would give out precise outputs, though few of the sensors receive wrong inputs or faulty sensors. This algorithm is relevant even today and has received the prestigious “Test of Time” award for its contribution over the decade by IEEE Congress in the year 2019.
Dr. Iyengar has received IEEE Fellow award, ACM Fellow, AAAS Fellow, Fellow of Artificial Intelligence AAIA, Fellow, National Academy of Inventors (NAI), Fellow of Institution of Engineers (India) among many awards he has received in his career. He also received IEEE Technical Achievement Award in 1998. Dr. Iyengar is awarded the Lifetime achievement award by International Society of Agile Manufacturing at IIT (BHU) in 2012. He received the Lifetime Achievement award from IEEE High Performance Computing in 2019. Dr. Iyengar was also a Fulbright Distinguished Scholar and has received several honorary PhDs from around the world.
He has been awarded the Lifetime Achievement Award for his contribution to the field of Digital Forensics on November 8, 2022, during the 7th INTERPOL DIGITAL FORENSICS EXPERT GROUP (DFEG) MEETING at National Forensics Sciences University, Gandhinagar, Gujarat, India.
Dr. Kun Sun is a Professor in the Department of Information Sciences and Technology at George Mason University. He is also the Associate Director of the Center for Secure Information Systems (CSIS) and the Director of Sun Security Laboratory. He received his Ph.D. in Computer Science from North Carolina State University. His research focuses on systems and network security. Dr. Sun has more than 15 years of working experience in both industry and academia, publishing over 120 conference and journal papers, and two papers won the Best Paper Award. His current research focuses on software security, network security, trustworthy computing, moving target defense, AI security, and cloud security. He won the Presidential Award for Faculty Excellence in Research from George Mason University in 2022.
Bibliographic Information
Book Title: AI Embedded Assurance for Cyber Systems
Editors: Cliff Wang, S.S. Iyengar, Kun Sun
DOI: https://doi.org/10.1007/978-3-031-42637-7
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-42636-0Published: 13 December 2023
Softcover ISBN: 978-3-031-42639-1Due: 13 January 2024
eBook ISBN: 978-3-031-42637-7Published: 12 December 2023
Edition Number: 1
Number of Pages: XVII, 246
Number of Illustrations: 18 b/w illustrations, 73 illustrations in colour
Topics: Computational Intelligence, Mobile and Network Security, Artificial Intelligence, Computer Crime, Cybercrime, Cyber-physical systems, IoT