Synopsis
As generative artificial intelligence (AI) evolves, it introduces new opportunities across industries, from content creation to problem-solving. However, with these advancements come significant cybersecurity risks that demand closer scrutiny. Generative AI, capable of producing text, images, code, and deepfakes, presents challenges in cybersecurity. Malicious scammers could leverage these technologies to automate cyberattacks, create sophisticated phishing schemes, or bypass traditional security systems with efficiency. This intersection of cutting-edge AI and cybersecurity concerns requires new organizational safeguards for digital environments, highlighting the need for new protocols, regulations, and proactive defense mechanisms to mitigate potential threats. Examining Cybersecurity Risks Produced by Generative AI addresses the intersections of generative AI with cybersecurity, presenting its applications, potential risks, and security frameworks designed to harness its benefits while mitigating challenges. It provides a comprehensive, up-to-date resource on integrating generative models into cybersecurity practice and research. This book covers topics such as deepfakes, smart cities, and phishing attacks, and is a useful resource for computer engineers, security professionals, business owners, policymakers, academicians, researchers, and data scientists.
About the Authors
Ammar Almomani is an Internet Infrastructures Security and Cybersecurity based on AI specialist with a Ph.D. Degree in Computer Science from Universiti Sains Malaysia (USM), which is ranked #142 in the QS World University Rankings 2021. He is recognized as One of the Top 2% of Global Influential Researchers by Stanford University and the Scopus Index-Oct-2023. His academic background also includes a Master of Science in Computer Information Systems and a Bachelor of Computer Science. He has an extensive publication record, with over 113+ articles published in reputable journals and conferences, most of them in Scopus Q1 and Q2 categories, and 8 patents and utility models published as six German utility models and Two India-grated patents. More than 21 articles and patents are under revision. He has significant experience in 20 years of teaching more than 42 topics with various subjects related to cybersecurity and Cybersecurity Engineering area such as “Network Security, Wireless Computer Networks security, Cryptography, Cloud computing, and security special topics (Ethical hacking) Enterprises Information System Security, Big data analytics, artificial intelligence, cybersecurity, networks, communications, and programming languages. with many international certificates. He taught at reputable 5 academic institutions, Supervising many undergraduate and postgraduate students' research projects and theses (Masters/Ph.D.) from many countries such as the UK, Malaysia, Jordan ..etc. As a cybersecurity and Internet infrastructure security specialist, he actively contributed to the field by managing AI and cybersecurity projects with international teams of top researchers and universities. He has authored 76 Scopus articles, H-index-20, 1380 citations, and garnered over 2,762 citations on Google Scholar, with an H-index of 27. Also he actively engaged in the academic community, with a strong online presence and a large readership and following on ResearchGate with about 89390 readers and followers. Currently working at Faculty - Computer Information Science, Higher Colleges of Technology, Sharjah, UAE. He has Five years of management experience with a UAE Golden Visa Holder, and my focus is on improving educational institutes. Moreover, he has made significant contributions to the development of courses and programs, including ABET and AACSB Accreditation
Mohammad Alauthman is an Associate Professor in the Information Security Department at the Faculty of Information Technology, University of Petra, Amman, Jordan. His research interests include network security, intrusion detection systems, and applying AI techniques―such as machine learning and deep learning―for botnet and DDoS detection, spam filtering, IoT security, and network traffic classification. He earned his Ph.D. in Computer Science from Northumbria University, UK, in 2016. Dr. Alauthman has received multiple research grants supporting advanced AI-driven intrusion detection systems and international collaboration. He is also actively involved in Erasmus+ projects, including BITTCOIN-JO (technology transfer), RL4Eng (remote engineering labs), Pro-GREEN LABs (green competences in education), and COMMO (mobility and cooperation across Mediterranean and Balkan institutions), contributing to innovation, sustainability, and cross-border academic development.
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