Retraction Notice: ML Hybrid Approach for Intrusion Detection in Networks
Sakshi Ravindra Deshmukh, Chandrashekhar Mankar · 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) · 2022
As in today’s developing network environment there is threat of new type of attacks daily in the network. So, the network administration system is also needed to be updated regularly for upgradation of security level. One of the network packet monitoring system is Intrusion Detection Systems (IDS). The proposed model is designed using machine learning approach for detection of malicious activities of the network packets. For that NSL-KDD dataset is used. First of all, the dataset is normalized for reducing calculation complexity, further features are reduced using co-relation algorithm, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). In final step of proposed algorithm multilevel (ML) hybrid classifiers, based on support vector machine (SVM) and random forest (RF), are designed for classification of dataset into five attack categories i.e. DOS, U2R, R2L, Probe and Normal. As compared to some other multilevel classifier work the proposed algorithm proves its efficiency in terms of high accuracy and False Alarm Rate (FAR).