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Enhanced transductive support vector machine classification with grey wolf optimizer cuckoo search optimization for intrusion detection system
Author(s) -
Roopa Devi E.M.,
Suganthe R.C.
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4999
Subject(s) - cuckoo search , computer science , intrusion detection system , support vector machine , normalization (sociology) , artificial intelligence , data mining , preprocessor , machine learning , data pre processing , pattern recognition (psychology) , particle swarm optimization , sociology , anthropology
Summary These days, the Intrusion detection System (IDS) is the most talked topic among the scientist and researchers and many research is going on in IDS, which is firmly connected to the protected utilization of system administrations. IDS are an essential part of the security infrastructure. The previous research works are focused to detect the attacks efficiently but it is failed to produce more accurate classification results. To stay away from the previously mentioned issues, in the proposed framework, Hybrid Grey Wolf optimizer Cuckoo Search Optimization (HGWCSO) along with Enhanced Transductive Support Vector Machine (ETSVM) is proposed. This exploration incorporates the modules are, for example, preprocessing, selection of features and classification of features. The processing of data is done by using normalization technique by using min‐max technique the main work is to replace the value missed and filters the features from NSL KDD dataset values. The main objective of processing of data is to increase the accuracy of classification. Then, the more relevant and optimal features are selected by using HGWCSO. The GWO robustness and searching performance is increased by cuckoo search algorithm. Then, the classification is performed to identify the intrusion attack types using ETSVM algorithm more efficiently. This classification algorithm is used to improve the attack detection accuracy higher. The experimental result concludes that the proposed HGWCSO with ETSVM algorithm provides better performance metrics in terms of high precision, sensitivity, specificity, and accuracy than the previous algorithms.