Detecting Distributed Denial of Service Attacks via Radial Basis Function Neural Networks

International Journal of Computer Science and Technology | Vol. 8, No. 2, February 2006 | pp. 9–16

DOI: 10.46882/2006/IJCST/080217

Research Article

Title: Detecting Distributed Denial of Service Attacks via Radial Basis Function Neural Networks

Names of Authors: Ahmed Al-Ghamdi¹, Linda Cartwright²

Authors’ Affiliations: ¹Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia; ²Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

Abstract:
Distributed Denial of Service (DDoS) attacks pose a severe threat to enterprise infrastructure by overwhelming internet servers with high volumes of malicious network traffic. This paper proposes a network-based intrusion detection engine that uses a Radial Basis Function (RBF) neural network to isolate malicious traffic anomalies in real time. The model extracts packet entropy values, IP source variation rates, and protocol distribution ratios from active network streams. The RBF model was trained on the benchmark DARPA intrusion dataset using a k-means clustering approach to optimize hidden layer center positions. The neural classifier achieved a true positive detection rate of 98.76% for flooding attacks (TCP SYN and UDP floods) while keeping the false positive rate minimal at 0.08%. The classification processing pipeline handled peak incoming rates of 85,000 packets per second, demonstrating its readiness for deployment within high-volume network security infrastructures.

Keywords: DDoS Detection, Radial Basis Function, Neural Networks, Network Security, Packet Entropy

Manuscript Timeline: Received November 05, 2005; Revised December 14, 2005; Accepted January 10, 2006; Published February 23, 2006

Citation: Al-Ghamdi, A., & Cartwright, L. (2006). Detecting Distributed Denial of Service Attacks via Radial Basis Function Neural Networks. International Journal of Computer Science and Technology, 8(2), 9–16.

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