Education
🎓 Ashfaq Ahmad Najar is nearing the completion of his Ph.D. in Computer Science from the Central University of Kerala, having submitted his thesis. He holds a Master’s degree in Information Technology from the Central University of Kashmir and a Bachelor’s degree in Information Technology from the University of Kashmir. His early education was completed at the J&K State Board of School Education.
Experience
💼 Ashfaq has a robust professional background, currently holding the position of IT Manager and Faculty at VIT Bhopal University. He has previously worked as an IT Manager Head and Tutor at KCCSBA Bijbehera, Anantnag, and as a Guest Faculty at Govt Boys Degree College Anantnag. His experience is complemented by his active participation in various workshops, seminars, and hackathons.
Research Interest
🔍 Ashfaq’s research interests are focused on Cyber Security, specifically Distributed Denial of Service (DDoS) attacks, as well as Machine Learning and Deep Learning. His work aims to develop robust systems to detect and mitigate cyber threats, leveraging advanced neural network architectures and machine learning algorithms.
Awards
🏆 Ashfaq has received several accolades, including the UGC-NET 2023 qualification, the GATE 2018 qualification, and the CUK University Fellowship for his Ph.D. research. He was also honored with the Young Researcher Award for his work on DDoS attack detection using MLP and random forest algorithms, and recognized as the best all-rounder in a cricket tournament organized by the Central University of Kerala.
Publications Top Notes
📝 Ashfaq Ahmad Najar has published numerous research articles in prestigious journals. Some of his notable works include:
A novel CNN-based approach for detection and classification of DDoS attacks. Concurrency Computat Pract Exper, 2024, Wiley. Cited by 4 articles
A robust DDoS intrusion detection system using convolutional neural network. Computers and Electrical Engineering, 2024, Elsevier. Cited by 8 articles
Cyber-secure SDN: A CNN-based approach for efficient detection and mitigation of DDoS attacks. Computers & Security, 2024, Elsevier. Cited by 6 articles
DDoS attack detection and mitigation using deep neural network in SDN environment. Computers & Security, 2024, Elsevier. Cited by 3 articles
DDoS attack detection using MLP and random forest algorithms. International Journal of Information Technology, 2022, Springer. Cited by 5 articles
