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Members of the Intrusion Detection and Prevention Systems (IDPSs) Research Group at the University of Jordan Publish a Number of Prestigious Scientific Papers in High-Impact International Journals

The Intrusion Detection and Prevention Systems (IDPSs) research group at the University of Jordan is dedicated to developing advanced cybersecurity solutions by integrating artificial intelligence, machine learning, and optimization techniques. Their research targets the enhancement of intrusion detection and prevention systems, with a special focus on critical infrastructures such as IoT, smart grids, and cloud environments. By leveraging emerging technologies—including deep learning and quantum computing—the group aims to reduce false positives and improve threat detection accuracy. Comprising a multidisciplinary team of experts in computer science, cybersecurity, and AI, the group actively contributes to the global research community.
Building on this strong research foundation, members of the IDPSs group have reached notable academic milestones by publishing a series of scientific papers in prestigious international journals ranked among the top tiers in databases such as Web of Science and Scopus. These publications highlight the University of Jordan's leadership in fields like artificial intelligence, information security, cloud computing, and the Internet of Things (IoT), reflecting both the quality and impact of the group's scholarly contributions. The published works include:

1. ​AI-driven job scheduling in cloud computing: a comprehensive review

• Journal: Artificial Intelligence Review – Springer
• Impact Factor (WoS): 10.7
• Scopus Percentile: 96%

                2. ​A Review of 6G and AI Convergence: Enhancing Communication Networks with Artificial Intelligence

• Research link: https://ieeexplore.ieee.org/document/10935636.

• Journal: IEEE Open Journal of the Communications Society – IEEE
• Impact Factor (WoS): 6.3
• Scopus Percentile: 94%

3. A crossover-integrated Marine Predator Algorithm for feature selection in intrusion detection systems within IoT environments
   • Research Link: https://www.sciencedirect.com/science/article/abs/pii/S2542660525000496.

• Journal: Internet of Things (The Netherlands) – Springer
• Impact Factor (WoS): 6.0
• Scopus Percentile: 96%

4. Recent advances in Multi-objective Cuckoo Search Algorithm, its variants and applications
   • Research Link: https://link.springer.com/article/10.1007/s11831-025-10240-9.

• Journal: Archives of Computational Methods in Engineering – Springer
• Impact Factor (WoS): 9.7
• Scopus Percentile: 97%

5. A deep learning-driven multi-layered steganographic approach for enhanced data security
   • Research Link: https://www.nature.com/articles/s41598-025-89189-5.

• Journal: Scientific Reports – Nature
• Impact Factor (WoS): 3.9
• Scopus Percentile: 92%

6. An enhanced method for intrusion detection systems in IoT environment
   •Research Link: https://link.springer.com/article/10.1007/s10586-024-04888-4.

• Journal: Cluster Computing – Springer
• Impact Factor (WoS): 3.6
• Scopus Percentile: 87%
These accomplishments come as part of the University of Jordan's support for scientific research and its encouragement of researchers to actively contribute to the production and dissemination of knowledge through reputable global journals and publishing houses.​