Enhanced intrusion detection through dual reduction and robust mean

Abstract

The exponential growth of online networks necessitates a paradigm shift in intrusion detection systems (IDS). Traditionalmethods falter under the massive influx of data, resulting in high false positives and reduced detection accuracy. This research introduces a novel approach combining principal component analysis (PCA) and linear discriminant analysis (LDA), augmented by robust generalized sample mean, to enhance IDS performance. PCA efficiently reduces data dimensionality, while LDA extracts critical features that differentiate normal network traffic from anomalies. The robust generalized sample mean counteracts the influence of outliers, ensuring accurate and reliable analysis. Implemented on the UNSW-NB15 dataset, our method achieves an average 6% reduction in false positives and a 10% increase in detection rate. Additionally, our testing methodology closely mirrors real-world conditions, making the results more representative of practical scenarios compared to existing work. These advancements demonstrate substantial improvements in IDS performance and robustness over existing techniques.