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Title |
Multilayer perceptrons networks for an Intelligent Adaptive intrusion detection system |
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Author |
Aida O. Ali, Ahmed saleh, Tamer Ramdan |
| Citation |
Vol. 10 No. 2 pp. 275-279 |
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Abstract |
Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An IDS’s responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. We need to use the classification algorithms to discriminate between normal and different types of attacks. The performance of nine artificial neural networks (ANNs) based classifiers was evaluated, based on a selected set of features. The results showed that; the Multilayer perceptrons (MLPS) based classifier provides the best results; about 99.63% true positive attacks are detected using this classifier. |
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Keywords |
Component, Intrusion detection syste, artificial neural network, Multilayer perceptrons |
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