WITHDRAWN: Kulczynski indexed dragonfly feature optimization based Polytomous Adaptive Base classifier for anomaly intrusion detection

Abstract

Abstract Anomaly-based intrusion detection plays a major role in the rapid growth of network technology to monitor the network activity and to protect from attacks. Recently, the machine learning techniques have been applied to build intrusion detection systems (IDS) for anomaly detection. Most of the works failed to find different types of attacks with minimum computation time. Kulczynski Similarity Indexed Dragonfly Optimization based Polytomous Adaptive Base Class Ensemble (KSIDO-PABCE) is introduced for anomaly intrusion detection in WSN to provide higher accuracy and lesser time consumption. KSIDO-PABCE technique comprised feature selection and classification. The feature selection is carried out using KSIDO. The designed optimization is a meta heuristic technique to determine the optimally better solution in search space depending on the behaviour of seeking the food source. The optimization technique generates the initial population of dragonflies (i.e. features) in search space. After initialization process, fitness of each dragonfly is determined by Kulczynski similarity index. Depending on fitness evaluation, the optimal features are chosen. With selected optimal features, PABCE technique is used to find different types of attacks. The ensemble classifier employs the weak learners as Support Vector Machine (SVM) for recognizing the suspicious activities to categorize data into normal or anomalous. After finding the anomalous, different types of attacks are correctly identified. Experimental evaluation with discussed results proves that the KSIDO-PABCE technique is more efficient compared to the existing state-of-art approaches.