An Evolutionary Feature Clustering Approach for Anomaly Detection Using Improved Fuzzy Membership Function
| Autor: | Mangathayaru Nimmala, Gunupudi Rajesh Kumar, Narsimha Gugulothu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
business.industry Computer science 020206 networking & telecommunications Pattern recognition 02 engineering and technology Fuzzy membership function Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence business Cluster analysis |
| Zdroj: | International Journal of Information Technology and Web Engineering. 14:19-49 |
| ISSN: | 1554-1053 1554-1045 |
| Popis: | Traditionally, IDS have been developed by applying machine learning techniques and followed single learning mechanisms or multiple learning mechanisms. Dimensionality is an important concern which affects classification accuracies and eventually the classifier performance. Feature selection approaches are widely studied and applied in research literature. In this work, a new fuzzy membership function to detect anomalies and intrusions and a method for dimensionality reduction is proposed. CANN could not address R2L and U2R attacks and have completely failed by showing these attack accuracies almost zero. Following CANN, the CLAPP approach has shown better classifier accuracies when compared to classifiers kNN, and SVM. This research aims at improving the accuracy achieved by CLAPP, CANN, and kNN. Experimental results show accuracies obtained using proposed approach is better when compared to other existing approaches. In particular, the detection of U2R and R2L attacks to user accuracies are recorded to be very much promising. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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