Evolutionary approach for intrusion detection
Gunupudi Rajesh Kumar, Nimmala Mangathayaru, G. Narsimha, Gali Suresh Reddy · 2017
In this work, we design and propose an improved fuzzy membership function to detect anomalies and intrusions. The objective of the present approach is to achieve an optimal transformation matrix which can improve classifier accuracies. The transformation matrix is aimed at mapping the original process onto a new fuzzy space, so that the resultant representation is free from noise data and facilitates to improve the overall accuracy and also individual class accuracies. Experimental results show that accuracies obtained using our approach is better compared to other approaches. In particular U2R and R2L accuracies are recorded to be very much promising. This research shows an approach which addresses the improvement in overall accuracy and also improvement in detecting R2L and U2R attack accuracies. In future, we plan to extend this work by applying new measures for dimensionality reduction and classification for improving U2R and R2L attack classification accuracies.