Feature Selection Intrusion Detection System for The Attack Classification with Data Summarization

Abstract

            The development of the communication technology leads to the improved network infrastructure for the data processing. The cloud wireless communication system comprises of the different vulnerabilities for the attack environment. Malicious attacks in the network subjected to the infrastructure advancement, infrastructure, environment, and economic loss. Intrusion Detection System (IDS) exhibits the effective model for the attack detection and classification in the network. This paper developed an effective attack classification scheme with the IDSSM involved in the feature selection. The proposed ISSM model comprises of the data summarization model for the feature selection of the attributes in the attack’s dataset. The performance evaluation of the proposed IDSSM model is evaluated for the NSL-KDD dataset for the attack classification. The performance of IDSSM is evaluated for the different dataset with the analysis expressed that proposed detection rate is measured as the 0.99 and the precision is measured as the 0.99. Additionally, the proposed IDSSM model exhibits the minimal data measurement model.