Guest Editors
Qasem Abu Al-Haija
Cybersecurity Department, Jordan University of Science and Technology
Dr. Qasem Abu Al-Haija is the head of the Cybersecurity Department at Jordan University of Science and Technology in Irbid, Jordan. He earned his PhD in computer engineering from Tennessee State University, USA, specializing in cybersecurity and intelligence. With over 200 publications to his name, Dr. Abu Al-Haija has been recognized among the world’s top 2% of scientists by Stanford University and Elsevier for his impactful contributions across cybersecurity, cryptography, artificial intelligence (AI), and the Internet of Things (IoT). His work is dedicated to advancing research and innovation in cybersecurity, AI, IoT, cyber-physical systems (CPS), and computer arithmetic.
Mahmoud Hammad
Department of Information Technology, Ajman University
Dr. Mahmoud Hammad is an associate professor in the Department of Information Technology at the College of Engineering and IT at Ajman University. Before joining Ajman University, Dr. Hammad worked as an associate professor in the Software Engineering Department within the Computer and Information Technology School at the Jordan University of Science and Technology (JUST). He also served as the director of the Center for E-Learning and Open Educational Resources at JUST. Dr. Hammad’s research interests are software engineering, specifically in software architecture, self-adaptive software systems, mobile computing, software analysis, software security, natural language processing, and machine learning. He earned his PhD in software engineering from the University of California, Irvine (UCI) under the supervision of Prof. Sam Malek. Before his doctorate, Hammad received his M.S. in Software Engineering from George Mason University, VA, USA, and his B.S. in Computer Science from Yarmouk University, Jordan.
Ahmad Alzu’bi
Department of Computer Science, Jordan University of Science and Technology
Ahmad Alzu’bi (Senior Member, IEEE; ACM Member) earned his PhD in computer science in 2016 from the University of the West of Scotland (UWS) in Paisley, United Kingdom. He is currently an associate professor in the Department of Computer Science at Jordan University of Science and Technology. His research interests encompass artificial intelligence, computer vision, and healthcare technologies. Alzu’bi has numerous research publications in reputable journals and conferences. He is a member of several professional and scientific organizations, committees, and councils. Alzu’bi has played a key role in developing new academic curricula in computing and artificial intelligence, as well as organizing professional conferences, workshops, and lectures. He has participated in various duties, including teaching, supervision, administration, research, and community service.
Mohammed Abdul Matheen
Applied College, Saudi Electronic University
Dr. Mohammed Abdul Matheen received his PhD in wireless sensor multimedia networks from VIT, Vellore, Tamil Nadu, and has more than 14 years of teaching and research experience. He serves as the academic and R&D supervisor at Applied College, Saudi Electronic University in Riyadh, Saudi Arabia. Previously, he has held several academic leadership roles, including head of department, EdTech leader, and exam supervisor. His research interests include wireless sensor networks, artificial intelligence, neural networks, IoT, and educational technology. He has supervised numerous undergraduate and postgraduate projects, authored and co-authored several research articles published in SCIE and Scopus-indexed journals, and presented papers at IEEE and other international conferences. Dr. Matheen is actively bridging academia with technology by exploring the role of AI-powered tools and automation in advancing education and applied research.
Collection Overview
Generative artificial intelligence (AI) is transforming cybersecurity by introducing unprecedented threats and innovative defense opportunities. Large language models (LLMs), generative adversarial networks (GANs), and diffusion models can now create highly realistic phishing emails, deepfakes, polymorphic malware, and automated exploit scripts. At the same time, these technologies can be harnessed for cyber defense, including intelligent threat simulation, automated incident response, deception frameworks, and advanced intrusion detection.
This Methods Collection will focus on experimental and reproducible methodologies at the intersection of generative AI, cybersecurity, and software engineering. Submissions are encouraged that provide detailed protocols for the following:
- Generating and evaluating adversarial content to stress-test security systems
- Leveraging generative AI for red-teaming and cyber range simulations
- Employing LLMs and generative agents for automated detection, response, and deception
- Integrating generative AI into secure software pipelines to enhance testing and vulnerability discovery
By combining diverse experimental approaches, this collection will provide the research community with practical methods, benchmarks, and best practices for studying and deploying generative AI responsibly in cybersecurity contexts. It will also serve as a foundation for building trustworthy, adaptive, and resilient defense systems that can evolve alongside emerging AI-driven threats.
Ultimately, this collection aims to bridge the gap between cutting-edge generative AI research and its secure application in software engineering, shaping the next generation of defensive and offensive cybersecurity methodologies.
