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All Journal INOVTEK Polbeng - Seri Informatika
Viky Luffiandi Rismawan
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Computer Science & IT

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Network Intrusion Detection System Using Convolutional Neural Network and Random Forest Classifiers Viky Luffiandi Rismawan; Pramudya, Elkaf Rahmawan
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/rxd38a11

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
Co-Authors Elkaf Rahmawan Pramudya