Artificial Intelligence and Cybersecurity

This book discusses artificial intelligence (AI) and cybersecurity from multiple points of view. The diverse chapters reveal modern trends and challenges related to the use of artificial intelligence when considering privacy, cyber-attacks and defense as well as applications from malware detection to radio signal intelligence.
The chapters are contributed by an international team of renown researchers and professionals in the field of AI and cybersecurity.
During the last few decades the rise of modern AI solutions that surpass humans in specific tasks has occurred. Moreover, these new technologies provide new methods of automating cybersecurity tasks. In addition to the privacy, ethics and cybersecurity concerns, the readers learn several new cutting edge applications of AI technologies.
Researchers working in AI and cybersecurity as well as advanced level students studying computer scienceand electrical engineering with a focus on AI and Cybersecurity will find this book useful as a reference. Professionals working within these related fields will also want to purchase this book as a reference.

Presents a fresh insight into the developing combination of AI and cybersecurity Discusses a variety of threats and defense methods Provides multiple viewpoints from society, industry and technology

Auteur
Tuomo Sipola works as a senior researcher at the Institute of Information Technology at JAMK University of Applied Sciences, Jyväskylä, Finland. His research interests include machine learning and data analytics, and he has worked in both research and commercial projects in many application areas, including predictive maintenance, network security and medical imaging. This earlier work experience includes research positions at the University of Jyväskylä and CEO duties in his startup company, CAP Data Technologies. He completed his PhD in mathematical information technology (University of Jyväskylä) in 2013, and recently, MA in Latin in 2020.
Tero Kokkonen works as a principal lecturer in cybersecurity and as the head of R&D in cybersecurity and artificial intelligence related R&D programs at JAMK University of Applied Sciences, Jyväskylä, Finland. Tero holds a PhD in information technology and has been promoted to adjunct professor (title of docent) in cybersecurity. Tero has significantly conducted research and development in cybersecurity and artificial intelligence including several international publications. Before his academic career, Tero has served as a program manager and technical specialist with C4ISR Systems in Finnish Air Force.
Mika Karjalainen is working at JAMK University of Applied Sciences as the director of School of Technology. Dr. Karjalainen also leads Institute of Information Technology's cybersecurity research, development and training center JYVSECTEC, which maintains and develops the Finnish national cybersecurity range, and has organized the Finnish National Cyber Security Exercise annually since 2013. Dr. Karjalainen holds a PhD in information technology and in conducting research in the field of cybersecurity education, especially focusing on students' learning during the cybersecurity exercises.

Contenu
Part I Cybersecurity Concerns.- Use of Artificial Intelligence in a Cybersecurity Environment.- Petri Vähäkainu and Martti Lehto.- A Review of Cyber Threat (Artificial) Intelligence in Security.- Management.- Martin Lundgren and Ali Padyab.- Model Based Resilience Engineering for Design and Assessment of.- Mission Critical Systems Containing AI Components.- Automation of CybersecurityWork.- Artificial Intelligence for Cybersecurity Education and Training.- Offensive Machine Learning Methods and the Cyber Kill Chain.- Defensive Machine Learning Methods and the Cyber Defence Chain.- Part II Privacy and Ethics.- Differential Privacy: An Umbrella Review.- AI in Cyber Operations: Ethical and Legal Considerations for End-Users.- Part III Applications.- Android Malware Detection Using Deep Learning.- Artificial Intelligence Enabled Radio Signal Intelligence.- Deep Learning Quantile Regression for Robustness, Confidence and.- Planning.- Model Fooling Threats Against Medical Imaging.