Indian Journal of Science and Technology

Abstract

Objectives: The purpose of semi-supervised clustering-based feature selection with the multiobjective genomic search class based classification process is to extract the intrusion features from the content of the unbalanced class field and structures in the dataset. Method: A class-based taxonomy with Multiobjective Genomic Search Method (SCMGSM) is semi-supervised Clustering Based Feature Selection designed to avoid inappropriate classification and to generate class based efficient authenticity for detecting intrusions. The proposed SCMGSM operates on a cluster class basis to reduce the overhead distribution by utilizing the subset classifications based on labelling process. For cluster class based feature selection there is not necessary to retain the interconnected features, so the proposed taxonomy to detect the attack features for each subset classification improves the performance. SCMGSM protects the data source using the potential blocking and minimizes the hassle of unauthorized users from changing features. Findings: The experiments focused on a set of data to identify and classify features based on each class. The proposed SCMGSM handles enhanced classification accuracy with 5.37% normal class completeness, reduces false positive rate to 2.78%, improves detection rate to 6.75% and reduces the backup issue by 10.65% compared to overhead classification. Novelty: SCMGSM operates based on multi objective genomic search based cluster class taxonomy and offers high degree of accuracy, classification and low false-positive rating compared to the detection of exploitation, Fuzzers, Generic and Renaissances attack classes with in a specific subset or a group of features.

Keywords: Classification; Cluster; Feature selection; Intrusion detection; Network attacks