A LogitBoost-based Algorithm for Detecting Known and Unknown Web Attacks

What is it about?

The rapid growth in the volume and importance of web communication throughout the Internet has heightened the need for better security protection. Security experts, when protecting systems, maintain a database featuring signatures of a large number of attacks to assist with attack detection. However, used in isolation, this can limit the capability of the system as it is only able to recognise known attacks. To overcome the problem, we propose an anomaly based intrusion detection system using an ensemble classification approach to detect unknown attacks on web servers.

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Why is it important?

In this paper we have presented the Logitboost-based classifier for detecting known and unknown web attack traffic. The proposed approach was evaluated using two publicly available labelled intrusion detection evaluation data sets NSL-KDD and UNSW-NB15 to allow different integration testing environments. In pre-processing, redundant and irrelevant features were filtered-out to obtain the most prominent features.