Online Anomaly Detection by Using N-gram Model and Growing Hierarchical Self-Organizing Maps

被引：0

作者：

Zolotukhin, Mikhail ^{[1
]}

Hamalainen, Timo ^{[1
]}

Juvonen, Antti ^{[1
]}

机构：

[1] Univ Jyvaskyla, Dept Math Informat Technol, FI-40014 Jyvvaskyla, Finland

来源：

2012 8TH INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC) | 2012年

关键词：

Data mining; intrusion detection; anomaly detection; n-gram; growing hierarchical self-organizing map;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 web-server. The system proposed is self-adaptive and allows detection of online malicious attacks in the case of continuously updated web-applications. The method is tested with network logs, which include normal and intrusive requests. Almost all anomalous requests from these logs are detected while keeping the false positive rate at a very low level.

引用

页码：47 / 52

页数：6

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