Unsupervised machine learning methods for finding anomalies in real-time web sessions

Alexandra Zyablitseva

Abstract


Detecting anomalies in web sessions is an important issue that is currently actively developing. To detect cyber threats, the signature-based anomaly detection method is generally used. Such methods effectively detect known attacks, but cannot detect unknown anomalies. In addition, existing anomaly detection methods are usually based on identifying abnormal sessions based on static data, having training data with tags, and also determine the session based on the IP address. In this paper, the main methods of collecting information about user actions on the network, forms of logging and collecting information about web sessions are considered, an approach to finding anomalies in web sessions is presented and justified. The anomaly detection model in web sessions uses an unsupervised clustering algorithm that does not require pre-marinated data, and the most effective clustering uses 13 of the most useful session parameters. The architecture of the developed approach is described for the presented model. At the end of the work, the result of the tethering is presented and further directions for the development of the project are described.


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References


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