Network Intrusion Detection System Using Artificial Intelligence

Abstract

This paper focuses on the development of an Advanced Network Intrusion Detection System (ANIDS) that leverages a combination of Machine Learning Algorithms, including Recurrent Neural Networks (RNN), K- Nearest Neighbours (KNN), CatBoost and AdaBoost to enhance the accuracy and efficiency of Intrusion Detection over Wireless Network. By Employing the fusion approach, the system aims to capitalize on the strengths of each algorithm to improve overall performance in identifying malicious activities and potential threats within Network Traffic. This paper utilizes scalar encoding for effective feature representation and applies Synthetic Minority Over Sampling Technique (SMOTE) to address class imbalance in the dataset, ensuring a more robust and fairer training process. Through a comprehensive training code and processing test code, the system is designed to accurately classify normal and abnormal network behaviour, significantly reducing false positives and improving detection rates. This Innovative approach not only enhances the security of network infrastructures but also provides a scalable solution for real time monitoring and response to cybersecurity threats, thereby contributing to safer digital environments