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.

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

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

[3]  Steven Cheung,et al.  The threat from the net [Internet security] , 1997 .

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

[5]  A.M. Cansian,et al.  Neural networks applied in intrusion detection systems , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[6]  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.

[7]  Marc Dacier,et al.  Intrusion detection , 1999, Comput. Networks.

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

[9]  Cannady,et al.  An Adaptive Neural Network Approach to Intrusion Detection and Response , 2000 .

[10]  Alfonso Valdes,et al.  Next-generation Intrusion Detection Expert System (NIDES)A Summary , 1997 .

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