A NOVEL MACHINE LEARNING TECHNIQUE FOR CYBERSECURITY NETWORK INTRUSION DETECTION SYSTEM

Abstract

Network intrusion is the unauthorized access, manipulation, or interruption of computer networks and systems. It comprises a variation of illegal behaviors, such as hacking, virus deployment and data theft. Detecting and blocking intrusions is vital for protecting sensitive data and credentials to ensure the integrity and safety of digital infrastructure. To develop a Machine Learning technique for a cybersecurity Network Intrusion Detection System, this research proposes a novel Cheetah Optimization-driven Intelligent Adaptive Boosting (CO-IAB) technique to protect systems and ensure data identification against threats and breaches by detecting and responding to unauthorized or illegitimate access to computer networks. Initially, the research obtained a dataset that contains a wide range of network intrusion techniques for training the suggested detection model. The collected raw data is pre-processed using Zscore Normalization to improve the quality of the data obtained. The Kernel Principal Component Analysis (Kernal-PCA) algorithm is employed to extract significant features from the processed data. The CO is used to optimize hyperparameters and feature selection for the subsequent IAB network architecture, improving its performance in network intrusions detection by purposely developed Python code. The analysis of the findings is evaluated with metrics parameters such as Recall, Precision, Accuracy and F1- score. The results are cross validated to authenticate the contribution of the proposed prediction model. Experimental findings show that the recommended prediction model has outperformed conventional approaches in IDS to secure network data