Enhancing Network Intrusion Detection with Deep Learning: A Comprehensive Analysis

With the growing sophistication of cyberattacks, traditional intrusion detection systems (IDS) are often inadequate. This research addresses these shortcomings by leveraging deep learning techniques to enhance the detection of network intrusions. Previous studies have either retained unnecessary features, leading to biased results, or overly simplified feature sets, resulting in low accuracy. This study systematically refines feature selection and employs advanced deep learning models to improve detection performance. The result is a robust IDS that mitigates bias and enhances accuracy, offering a significant improvement over existing methods. Our findings provide valuable insights into the effective application of machine learning for network security.