Improving intrusion detection for better anomaly detection based on x-means clustering and multi-layer perceptron classification
Due to excessive usage of network communication through the Internet with sensitive data in recent years, providing competent security medium to secure this data has become the most matters to be considered. One of the significant security mediums is an Intrusion Detection System (IDS) which o...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/66741/1/FSKTM%202016%2028%20IR.pdf |
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| Summary: | Due to excessive usage of network communication through the Internet with
sensitive data in recent years, providing competent security medium to secure this data has
become the most matters to be considered. One of the significant security mediums is an
Intrusion Detection System (IDS) which offers anomaly detection with the proficiency to
recognize unforeseen attacks. An IDSs should provide high accuracy, detection rates and
low false alarm rate, but yet the majority of previous IDSs approaches suffered from the
average rate of accuracy and detection as well as with high rate of false alarm .To enhance
the capability of IDS, this thesis proposed a new hybrid machine learning approach based
on X-Means and Multilayer perceptron called XM-MLP. X-Means used to cluster the data
according to its behavior while multilayer perceptron (MLP) Neural Network classify
those data into correct categories i.e. attack or normal. ISCX 2012 benchmark dataset has applied to evaluate the proposed hybrid approach against single MLP classifier and
previous hybrid approaches such as KM-MLP, XM-1R and XM-NB where the core
detection method is based on clustering or classification technique. The performance of the
proposed hybrid approach achieves better result from a single MLP classifier and other
hybrid approaches in term of accuracy, detection and false alarm rate. |
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