An efficient feature reduction method for the detection of DoS attack

Feature selection or reduction is a significant process for intrusion detection system (IDS) in finding optimal features. Irrelevant features present in the dataset increase load on computing resources and affect the performance of the system. The present study proposes a feature reduction method based on the combination of filter-based feature reduction algorithms, namely Information Gain Ratio (IGR), Correlation (CR), and ReliefF (ReF). The system initially obtains feature subsets for each classifier based on average weight and further Subset Combination Strategy (SCS) is applied. The proposed feature reduction method results in 24 reduced features for CICIDS 2017 DoS dataset. The proposed method shows an improved performance compared to the current state-of-the-art systems on CICIDS 2017 dataset. The proposed method has also been tested and compared with the current state-of-the-art systems on KDD Cup 99 dataset. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.

Automated summary

The study suggests a feature reduction method by combining filter-based algorithms to enhance the performance of intrusion detection systems, achieving effective feature reduction for the CICIDS 2017 DoS dataset compared to current systems.