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Using Of Neuro-Fuzzy Classifier for Intrusion Detection Systems
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Mohammad Hassan Nataj Solhdar *   |
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Abstract: (1797 Views) |
One of the tools of artificial intelligence is the adaptive neural-fuzzy inference system (ANFIS), which is used in this article to build an intrusion detection system and we call it the neural-fuzzy classifier. The Intrusion Detection System based on ANFIS is an anomaly based intrusion detection system that uses fuzzy logic and neural network to detect if malicious activity is taking place on a network. This paper describes the architecture of the ANFIS and its components.
The sample fuzzy rules are developed for some kinds of attacks and the testing results with actual network data are described. Our experiments and evaluations were performed with the NSLKDD intrusion detection dataset which is a version of the KDD Cup99 intrusion detection evaluation dataset prepared and managed by MIT Lincoln Laboratories.
Finally, this paper tries to show the efficiency of the designed model by examining the performance of the "neural-fuzzy adaptive inference system" model on a standard and comprehensive set.
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| Keywords: Intrusion Detection System, neural network, neuro-fuzzy classifier, ANFIS, NSLKDD |
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Full-Text [PDF 511 kb]
(256 Downloads)
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Type of Study: Research |
Subject:
Artificial Intelligence
Received: 2021/02/26 | Accepted: 2021/09/13 | Published: 2022/03/13
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