A framework for detection of cyber attacks by the classification of intrusion detection datasets

Abstract

Recognition of the consequence for advanced tools and techniques to secure the network infrastructure from the security risks has prompted the advancement of many machine learning-based intrusion detection strategies. However, it is a big challenge for the researchers to make improvements in an Intrusion Detection System with desired advantages and constraints. This paper has developed a proficient soft computing framework using Grey Wolf Optimization and Entropy-Based Graph (GWO-EBG) to classify intrusion detection datasets to reduce the false rate. In the proposed scheme, initially, the input data is preprocessed by the data transformation and normalization procedure. After the preprocessing, optimal features have been chosen for the dimension reduction from the preprocessed data using the grey wolf optimization (GWO) algorithm. Then, the Entropy value has estimated from the idyllically selected features. Lastly, an Entropy-Based Graph (EBG) has been constructed to classify data into intrusion or normal data. The experimental results demonstrate that the developed method outperforms other existing methods in various performance measures. The detection rate of the developed GWO-EBG is found to be 94.6%, which is higher than 91.24 % of EBG, 75.60 % K-Nearest Neighbors (KNN), 73.36 % of Support Vector Machine (SVM), and 74.88 % of Generalized Regression Neural Network (GRNN) on 5000 connection vectors data obtained from KDD CUP’99 testing dataset. The false-positive rate of developed strategy (GWO-EBG) is 0.35 %%, which is lower than 2.18 % of EBG, 7.32 % KNN, 8.15 % of SVM, and 8.13 % of GRNN with 5000 testing datasets.