Hybrid Machine Learning and XAI Approach for Real-Time Intrusion Detection

The growth of communication technologies and advancement in cloud computing have led to an increase in the possibility of cybercrime and attacks. Traditional IDSs (Intrusion Detection Systems) are ineffective in detecting sophisticated attacks that evolve rapidly and target information systems and therefore do not provide sufficient protection. Moreover, traditional systems are slow to adapt to the appearance of new threats and produce a high number of false alarms which are often considered a nuisance. The proposed study offers an intrusion detection system framework that incorporates the hybrid techniques of machine learning to enhance the security of networks and detect cyber threats. The key aspects of the system are the following: the model uses various machine learning algorithms as well as anomaly detection methods in order to detect intrusions effectively. The effectiveness of the framework in terms of its accuracy, precision, recall, F1 score, and false positive index calculation is measured through conducting empirical testing applying public datasets as references for verification of the effectiveness of the proposed model. The results of the study show that the hybrid intrusion detection system manages to achieve better performance in comparison with conventional solitary models due to its adaptability, low computational complexity and ability to provide security in the context of rapidly evolving networks. The developed intrusion detection system aims to meet the rising demand for intelligent solutions which provide security for modern communication systems.