Comparative Analysis of DNN, GBT, and KNN Models for Network Intrusion Detection
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Abstract
Network intrusion detection is critical to cybersecurity, aiming to identify and mitigate unauthorized access and attacks on computer systems and networks. This study evaluates the effectiveness of three machine learning techniques—deep neural networks (DNN), gradient boost trees (GBT), and k-nearest neighbors (KNN)—in detecting network intrusions. The performance of these models was assessed using a comprehensive dataset of 2,540,047 records encompassing 49 features across nine attack categories. The results indicate that GBT outperforms DNN and KNN in accuracy and robustness. These findings highlight the potential of GBT for enhancing intrusion detection systems and contribute valuable insights into the comparative performance of different machine learning algorithms in cybersecurity applications.
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