Abstract
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.
Highlights
The rapid and extensive growth of Internet technology increases the importance of protecting computer networks from attacks
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
Summary
The rapid and extensive growth of Internet technology increases the importance of protecting computer networks from attacks. Nowadays there exist different approaches for intrusion detection It is signature analysis, rulebased method, embedded sensors, neural networks, artificial immune systems [1, 2, 3, 4, 5, 6] and so on. This paper presents applying of neural networks for intrusion detection through an examination of network traffic data. It has been shown that denial of service and other network-based attacks are presented in the network traffic data. Using neural networks permits to extract nonlinear relationships between variables from network traffic and to design real-time intrusion detection systems.
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