Network Intrusion Detection System Using Convolutional Neural Network and Random Forest Classifiers

Abstract

Network Intrusion Detection Systems (NIDS) play a crucial role in protecting networks from various forms of cyberattacks. However, conventional signature-based methods often fail to detect new or unknown threats and are prone to generating high false positive rates. This study proposes a hybrid approach combining Convolutional Neural Network (CNN) and Random Forest (RF) to develop a more adaptive and accurate intrusion detection system. CNN is employed to extract features from raw network traffic data, while RF serves as the primary classifier. The UNSW-NB15 dataset is used for training and testing the model. Evaluation results show that the hybrid model achieves an accuracy of 93.0%, average precision of 94%, recall of 90%, F1-score of 92%, and a false positive rate of 19.2%. These results demonstrate that the CNN–RF hybrid approach effectively improves intrusion detection performance and offers a promising solution for modern network security systems