Anomaly Detection from Network Logs Using Diffusion Maps

Abstract : The goal of this study is to detect anomalous queries from network logs using a dimensionality reduction framework. The fequencies of 2-grams in queries are extracted to a feature matrix. Dimensionality reduction is done by applying diffusion maps. The method is adaptive and thus does not need training before analysis. We tested the method with data that includes normal and intrusive traffic to a web server. This approach finds all intrusions in the dataset.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.172-181, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_20〉
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Tuomo Sipola, Antti Juvonen, Joel Lehtonen. Anomaly Detection from Network Logs Using Diffusion Maps. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.172-181, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_20〉. 〈hal-01571332〉

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