Adaptive framework for network traffic classification using dimensionality reduction and clustering
Juvonen, A., & Sipola, T. (2012). Adaptive framework for network traffic classification using dimensionality reduction and clustering. In Y. Koucheryavy, J. Rak, J. P. G. Sterbenz, A. Vinel, V. Vishnevsky, & B. H. Walke (Eds.), IV International Congress on Ultra Modern Telecommunications and Control Systems 2012 (pp. 274-279). IEEE. International Conference on Ultra Modern Telecommunications & workshops. https://doi.org/10.1109/ICUMT.2012.6459678
© 2010 IEEE. This is an author's post-print version of an article whose final and definitive form has been published in the conference proceeding by IEEE.
Information security has become a very important topic especially during the last years. Web services are becoming more complex and dynamic. This offers new possibilities for attackers to exploit vulnerabilities by inputting malicious queries or code. However, these attack attempts are often recorded in server logs. Analyzing these logs could be a way to detect intrusions either periodically or in real time. We propose a framework that preprocesses and analyzes these log files. HTTP queries are transformed to numerical matrices using n-gram analysis. The dimensionality of these matrices is reduced using principal component analysis and diffusion map methodology. Abnormal log lines can then be analyzed in more detail. We expand our previous work by elaborating the cluster analysis after obtaining the low-dimensional representation. The framework was tested with actual server log data collected from a large web service. Several previously unknown intrusions were found. Proposed methods could be customized to analyze any kind of log data. The system could be used as a real-time anomaly detection system in any network where sufficient data is available. ...
Parent publication ISBN978-1-4673-2015-3
ConferenceInternational Congress on Ultra Modern Telecommunications and Control Systems
Is part of publicationIV International Congress on Ultra Modern Telecommunications and Control Systems 2012
ISSN Search the Publication Forum2157-0221
Publication in research information system
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Dimensionality reduction framework for detecting anomalies from network logs Sipola, Tuomo; Juvonen, Antti; Lehtonen, Joel (CRL Publishing, 2012)Dynamic web services are vulnerable to multitude of intrusions that could be previously unknown. Server logs contain vast amounts of information about network traffic, and finding attacks from these logs improves the ...
Anomaly detection from network logs using diffusion maps Sipola, Tuomo; Juvonen, Antti; Lehtonen, Joel (Springer, 2011)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 ...
Combining conjunctive rule extraction with diffusion maps for network intrusion detection Juvonen, Antti; Sipola, Tuomo (IEEE, 2013)Network security and intrusion detection are important in the modern world where communication happens via information networks. Traditional signature-based intrusion detection methods cannot find previously unknown ...
An Efficient Network Log Anomaly Detection System using Random Projection Dimensionality Reduction Juvonen, Antti; Hämäläinen, Timo (IEEE, 2014)Network traffic is increasing all the time and network services are becoming more complex and vulnerable. To protect these networks, intrusion detection systems are used. Signature-based intrusion detection cannot find ...
Online anomaly detection using dimensionality reduction techniques for HTTP log analysis Juvonen, Antti; Sipola, Tuomo; Hämäläinen, Timo (Elsevier BV * North-Holland; International Council for Computer Communications, 2015)Modern web services face an increasing number of new threats. Logs are collected from almost all web servers, and for this reason analyzing them is beneficial when trying to prevent intrusions. Intrusive behavior often ...