Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Improving intrusion detection performance using keyword selection and neural networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
A denial-of-service resistant intrusion detection architecture
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Security issues in control, management and routing protocols
Computer Networks: The International Journal of Computer and Telecommunications Networking - Pioneering tomorrow's Internet Selected papers from the TERENA Networking Conference 2000 22–25 May 2000, Lisbon, Portugal
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Statistical Traffic Modeling for Network Intrusion Detection
MASCOTS '00 Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
Analysis of a Denial of Service Attack on TCP
SP '97 Proceedings of the 1997 IEEE Symposium on Security and Privacy
DDoS attacks and defense mechanisms: classification and state-of-the-art
Computer Networks: The International Journal of Computer and Telecommunications Networking
DDoS attack detection method using cluster analysis
Expert Systems with Applications: An International Journal
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In this paper we present and evaluate a Radial-basis-function neural network detector for Distributed-Denial-of-Service (DDoS) attacks in public networks based on statistical features estimated in short-time window analysis of the incoming data packets. A small number of statistical descriptors were used to describe the DDoS attacks behaviour, and an accurate classification is achieved using the Radial-basis-function neural networks (RBF-NN). The proposed method is evaluated in a simulated public network and showed detection rate better than 98% of DDoS attacks using only three statistical features estimated from one window of data packets of 6 s length. The same type of experiments were carried out on a real network giving significantly better results: a 100% DDoS detection rate is achieved followed by a 0% of false alarm rate using different statistical descriptors and training conditions for the RBF-NN.