Enhancing Intrusion Detection Systems through Advanced Feature Engineering and Machine Learning Techniques

In this study, I explore advanced methodologies for improving intrusion detection systems (IDS) by leveraging machine learning algorithms and sophisticated feature engineering. I initially assess the performance of various classification algorithms, including k-nearest neighbors (KNN) and support vector machines (SVM), on the ADFA-LD 12 dataset. My analysis reveals that SVMs outperform other methods, particularly when using two-sequence feature spaces instead of traditional frequency-based approaches. I conduct a detailed evaluation of SVM performance with linear and sigmoid kernels, revealing that the two-sequence feature space significantly enhances detection accuracy. Recursive feature elimination demonstrates that optimal performance can be achieved with fewer than 240 features, underscoring the importance of effective feature selection. In contrast, one-class SVMs, used for outlier detection, show comparatively poor performance, indicating that traditional outlier detection methods may not be as effective in this context. My findings highlight the efficacy of SVM classifiers in both two-sequence and frequency feature spaces, though the latter does not offer substantial improvement. The study also emphasizes the need for further res...