Fuzzy and Real-Coded Chemical Reaction Optimization for Intrusion Detection in Industrial Big Data Environment

Analysis and modeling of the intrusion detection system is an important phenomenon for any communication network, which helps to monitor the network traffic and avoid suspicious activity in the Big Data environment. The machine learning approach for modeling the intrusion detection system requires analysis of large network data, which may include some irrelevant features resulting in unnecessary computational and analytical burden. In this article, a fuzzy and real coded chemical reaction optimization-based cluster analysis approach with feature selection is proposed for the intrusion detection system in a Big Data platform. The proposed cluster analysis model is achieved through Fuzzy C-Mean (FCM) with real-coded chemical reaction optimization, which boosts FCM to start with optimized cluster centers. Also, the use of the Flexible Mutual Information Feature Selection approach helps this model to avoid the processing of a large number of features, which drastically affects processing elements.

Automated summary

The article introduces a cluster analysis approach for intrusion detection system in Big Data platform, which utilizes feature selection to avoid processing a large number of features, thereby enhancing the model's efficiency.