Optimizing Network Intrusion Detection with a Hybrid Adaptive Neuro Fuzzy Inference System and AVO-based Predictive Analysis

Abstract

Protecting data and computer systems, as well as preserving the accessibility, integrity, and confidentiality of vital information in the face of constantly changing cyberthreats, requires the vital responsibility of detecting network intrusions. Existing intrusion detection models have limits in properly capturing and interpreting complex patterns in network behavior, which frequently leads to difficulties in robust feature selection and a lack of overall intrusion detection accuracy. The drawbacks of current methods are addressed by a unique approach to network intrusion detection presented in this paper. This framework discusses the difficulties presented by changing cyberthreats and the critical requirement for efficient intrusion detection in a society growing more networked by the day. Using a Hybrid Adaptive Neuro Fuzzy Inference System and African Vulture Optimization model with Min-Max normalization and data cleaning on the NSL-KDD dataset, the methodology outlined here overcomes issues with complex network behavior patterns and improves feature selection for precise identification of potential security threats. This approach meets the need for an effective intrusion detection system. Python software is used to implement the suggested model since it is flexible and reliable. The results show a notable improvement in accuracy, with the Hybrid Adaptive Neuro Fuzzy Inference System and African Vulture Optimization model surpassing previous approaches significantly and obtaining an exceptional accuracy rate of 99.3%. The accuracy of the proposed model was improved by African Vulture Optimization, rising from 99.2% to 99.3%. When compared to Artificial Neural Network (78.51%), Random Forest (92.21%), and Linear Support Vector Machine (97.4%), this amazing improvement is clear. When compared to other techniques, the suggested model exhibits an average accuracy gain of about 20.79%.