Bashar Ahmed Khalaf
Papers
1
Total Citations
18
H-Index
1
About
Bashar Ahmed Khalaf is a prominent researcher in the fields of cybersecurity and network defense, with a particular focus on adaptive intrusion detection and mitigation strategies. His most-cited work, "An Adaptive Model for Detection and Prevention of DDoS and Flash Crowd Flooding Attacks" (2018, 18 citations), introduces a novel multi-agent system that distinguishes between malicious distributed denial-of-service (DDoS) attacks and legitimate flash crowds, enabling real-time, automated prevention. This contribution addresses a critical gap in network security by leveraging software agents and feature extraction techniques to reduce false positives and enhance resilience. Khalaf’s research integrates multi-agent systems, mobile ad hoc networks, and computer vision, demonstrating a cross-disciplinary approach to solving complex security challenges. His work has been instrumental in advancing adaptive security models, earning recognition for its practical applicability in safeguarding Internet infrastructure. With a growing citation impact, Khalaf continues to influence both academic research and industry practices, making him a key figure in the evolution of intelligent, self-defending networks.
Research Focus
Key Achievements
Top Papers
- 1