Examining Cybersecurity Risks Produced by Generative AI. This item is unavailable.Examining Cybersecurity Risks Produced by Generative AI

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.

Professor Ammar Ali Almomani He is a Cybersecurity Based on AI, specialist with a Ph.D. Degree in advanced internet security and monitoring from Universiti Sains Malaysia (USM), 20 Years of Teaching and industrial experience, exposure to innovation and entrepreneurship. 6 years of management included around 3 years as Head of the Research and Innovation Department, - One of the Top 2% of Global Influential Researchers by Stanford University and the Scopus Index-2023-2024. - Consultant and Expert member in UAE Research Map (R&D) - I have an extensive publication record, with over 150 + articles published in reputable journals and conferences, most of them in Scopus Q1 and Q2 categories, 100 Scopus articles, Scopus citations=1950, H-index=22, and 10 patents published as Eight Germany and Two India granted patents. 4100 Citations on Google Scholar, with an H-index of 31. I-index= 68. - More than 20 articles, books, and conferences are under revision in 2024. worked in AL-Balqa Applied University -Jordan and Currently, working in the Computer Information Science (CIS) program, Bachelor of Cybersecurity, at Higher Colleges of Technology (HCT), Sharjah, UAE.

Dr. Mohammad Alauthman (mohammad.alauthman@uop.edu.jo) 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.