Detecting intruders in the network using machine learning classifier
Journal Title: Elysium Journal of Engineering Research and Management - Year 2016, Vol 6, Issue 2
Abstract
Rapid development in technology has raised the need for an effective intrusion detection system as the traditional intrusion detection method cannot compete against newly advanced intrusions. In the proposed work uses machine learning technique to detect both known and unknown attacks in the payload analysis of network traffic. As the majority of such systems, the proposal consists of two phases: a training phase and a detection phase. During the training phase the statistical model of the legitimate network usage is created through Bloom Filters and N-grams techniques. Subsequently, the results obtained by analyzing a dataset of attacks are compared with such model. This will allow a set of rules to be developed which will be able to detect whether the packets contain malware payloads. In the detection phase, the traffic is to analyze compared with the model created in the training phase and the results obtained when applying rules.
Authors and Affiliations
Nivedita S, Revathi. M. P
Keywords
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