Enhancing Cloud Security with a Fuzzy Rule-Based Classifier for Intrusion Detection and Classification

Cloud computing (CC) is a model of distributed computing that makes it possible to access data, applications, and computer infrastructure through the Internet whenever and wherever it is needed. CC is a method of providing online consumers with access to virtualized, dynamically scaled resources. The importance of safety in this on-demand CC cannot be overstated. That's why the authors of this study are presenting a new method of cloud intrusion detection using fuzzy rules. The IDFRC method can monitor the distributed CC platform for intrusions and protect it from any dangers. Each client has their own unique IDS instance installed, with its own dedicated controller. In order to better detect and categorize intrusions, the authors of this piece provide the improved intrusion detection using Fuzzy Rule-based Classifier (IDFRC) model for use in the cloud. The proposed ID-FRC paradigm seeks to distinguish between malicious and benign cloud-based data flows. The improved results of the suggested method are analyzed by a comprehensive simulation study. A hybrid intelligent fruit fly optimization algorithm (HFOA) is used to fine-tune the FRC model's parameters. The FRC is a powerful paradigm in pattern recognition that provides useful results by the incorporation of language labels into the rules' antecedents. The KDD99 and NSL-KDD dataset is utilized to evaluate the suggested approach. Improvements of the current method over recent state-of-the-art methods were guaranteed by a simulation study of the IDFRC model. Maximum detection performance was reported by the model, with an F-score of 98.5 and a reliability of 100%.