论文信息 - INTRUSION RECOGNITION USING NEURAL NETWORKS

INTRUSION RECOGNITION USING NEURAL NETWORKS

Intrusion detection techniques are of great importance for computer network protecting because of increasing the number of remote attack using TCP/IP protocols. There exist a number of intrusion detection systems, which are based on different approaches for anomalous behavior detection. This paper focuses on applying neural networks for attack recognition. It is based on multilayer perceptron. The 1999 KDD Cup data set is used for training and testing neural networks. The results of experiments are discussed in the paper.

Pavel A. Kochurko | V. Golovko

[1] Susan C. Lee,et al. Training a neural-network based intrusion detector to recognize novel attacks , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[2] Salvatore J. Stolfo,et al. A framework for constructing features and models for intrusion detection systems , 2000, TSEC.

[3] Anup K. Ghosh,et al. A Study in Using Neural Networks for Anomaly and Misuse Detection , 1999, USENIX Security Symposium.

[4] Hervé Debar,et al. A neural network component for an intrusion detection system , 1992, Proceedings 1992 IEEE Computer Society Symposium on Research in Security and Privacy.

[5] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[6] Diego Zamboni,et al. Data collection mechanisms for intrusion detection systems , 2000 .