Enhanced Machine Learning Approaches for Network Intrusion and Anomaly Detection

In today’s rapidly evolving digital landscape, network security has become a critical priority for businesses and organizations striving to protect sensitive data and systems from increasingly sophisticated cyber threats. Traditional firewalls and security measures are no longer sufficient, making advanced solutions like Network Intrusion Detection Systems (NIDS) essential. This paper explores the application of machine learning algorithms for detecting network intrusions and anomalies, focusing on a comparative analysis between single-stage and two-stage classification models. Using the NSL-KDD and UNSW-NB15 datasets, the study evaluates their effectiveness in identifying both known and novel cyber threats. Results indicate that two-stage classifiers enhance detection accuracy by efficiently recognizing anomalies and categorizing attack types. In the experiments, the best two-stage model (MLP $\rightarrow$ MLP) achieved a 17% higher recall and a 9% higher F1-score on average compared to its single-stage counterpart. These gains demonstrate that multi-stage classification can substantially reduce false negatives and improve overall intrusion detection efficacy. These findings show the potential of multi-stage classification models as a robust improvement in network intrusion detection, offering a promising direction for future research in cybersecurity and anomaly detection methodologies.