Neural network based intrusion detection systems with different training functions
Şahingöz, Özgür Koray
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In the last decades, due to the improvements in networking techniques and the increased use of the Internet, the digital communications entered all of the activities in the global marketplace. Parallel to these enhancements the attempts of hackers for intruding the networks are also increased. They tried to make unauthorized access to the networks for making some modifications in their data or to increase the network traffic for making a denial of service attack. Although a firewall seems as a good tool for preventing this type of attacks, intrusion detection systems (IDSs) are also preferred especially for detecting the attack within the network system. In the last few years, the performance of the IDS is increased with the help of machine learning algorithms whose effects depend on the used training/learning algorithm. Mainly it is really hard to know which learning algorithm can be the fastest one according to the problem type. The algorithm selection depends on lots of factors such as the size of data sets, number of nodes network design, the targeted error rate, the complexity of the problem, etc. In this paper, it is aimed to compare different network training function in a multi-layered artificial neural network which is designed for constructing an effective intrusion detection system. The experimental results are depicted in the paper by explaining the efficiency of the algorithms according to their true-positive detection rates and speed of the execution.