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Attacks against web servers and web-based applications remain a serious global network security threat. Attackers are able to compromise web services, collect confidential information from web data bases, interrupt or completely paralyze web servers. In this study, we consider the analysis of HTTP logs for the detection of network intrusions. First, a training set of HTTP requests which does not contain...
In this research, online detection of anomalous HTTP requests is carried out with Growing Hierarchical Self-Organizing Maps (GHSOMs). By applying an n-gram model to HTTP requests from network logs, feature matrices are formed. GHSOMs are then used to analyze these matrices and detect anomalous requests among new requests received by the webserver. The system proposed is self-adaptive and allows detection...
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