Development of a Network Intrusion Detection Model using Hybridised Machine Learning Algorithms

Cyber threats continue to grow in this era since the bad actors are attempting to exploit individuals, organisations, and systems. The latest development in artificial intelligence has unleashed strong agents at the fingertips of humanity. As open as it is, it has made more room for possible bad actors. Systems that can successfully counter these threat actors need to be created to rescue humanity. In this research work, RNN and Random Forest classifiers' hybridised models are combined for the development of a Network Intrusion Detection System (NIDS) based on the benchmark dataset (CICIDS 2017) The requirement for an efficient and accurate method to detect network intrusions, both known and zero-day anomalies, is the primary problem considered. This research aims to enhance the accuracy and reliability of intrusion detection systems through a hybrid modelling approach. For evaluating the performance of the proposed model, various measures like accuracy, precision, recall, F1 measure, true positive rate, and true negative rate were employed. The hybrid model showed very good results with testing accuracy of 96.08%, precision of 96.0%, and recall of 96.0%, along with an F1 measure of 96.0%. The result of the experiment indicates that the model is effective and, when implemented, can detect and classify cyberattacks in modern environments.