Comparative Analysis of ML/DL Approaches Using SMOTE-Based Data Balancing for Network Intrusion Detection

Detecting irregular patterns that suggest potential threats or system flaws plays a vital role in anomaly detection, which is essential for maintaining the security and integrity of network systems, especially in IoT environments where devices are often vulnerable and widely distributed. This study uses the UNSW-NB15 dataset, a vast collection of network traffic data, to examine several Machine Learning (ML) and Deep Learning (DL) approaches for network anomaly detection. In order to improve model performances, the study uses strategies such the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance. The capabilities of a number of widely used machine learning (ML) algorithms, such as Decision Trees, Random Forests, KNN, XGB, and widely used deep learning (DL) models, such as CNN, ANN and LSTM, to identify unusual patterns in various attack and typical behavior scenarios are assessed. Our findings provide important information for future advancements in anomaly detection techniques by highlighting the significance of feature selection, class balancing, and model resilience in successfully differentiating unusual behaviors in network traffic. XGB emerged as the most successful approach in this study, with its enhanced performance largely attributed to the synergy between its robust ensemble framework and the class balancing achieved through SMOTE.