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JAIT 2024 Vol.15(7): 886-895
doi: 10.12720/jait.15.7.886-895

AI Driven Anomaly Detection in Network Traffic Using Hybrid CNN-GAN

Vuda Sreenivasa Rao 1,*, R. Balakrishna 2, Yousef A. Baker El-Ebiary 3, Puneet Thapar 4,
K. Aanandha Saravanan 5, and Sanjiv Rao Godla 6
1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation,
Green Fileds, Vaddeswaram, India
2. Department of Artificial Intelligence and Data Science,
Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Inida
3. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin – UniSZA University, Malaysia
4. Computer Science and Engineering Department, Lovely Professional University, Punjab, India
5. VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India
6. epartment of Computer Science & Engineering, Aditya College of Engineering & Technology-Surampalem,
Andhra Pradesh, India
Email: vsreenivasarao@kluniversity.in (V.S.R.); r.balakrishna1989@gmail.com (R.B.);
yousefelebiary@unisza.edu.my (Y.A.B.E.-E.); puneet.thapar90@gmail.com (P.T.);
5anand23sarvan@gmail.com (K.A.S.); sanjiv_gsr@yahoo.com (S.R.G.)
*Corresponding author

Manuscript received November 28, 2023; revised January 22, 2024; accepted January 24, 2024; published July 29, 2024.

Abstract—As the complexity and sophistication of cyber threats continue to evolve, traditional methods of network anomaly detection fail to identify novel and subtle attacks. In response to this challenge, authors propose a novel approach to network anomaly detection utilizing a Hybrid Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architecture. The hybrid model leverages the strengths of both CNN and GAN to enhance the detection of network anomalies. The CNN component is designed to extract high-level features from network traffic data, allowing it to capture complex patterns and relationships within the data. Simultaneously, the GAN component acts as a generator and discriminator, learning to generate normal network traffic patterns and distinguishing anomalies from them. To train the hybrid model, employing a large dataset of labelled network traffic, encompassing both normal and anomalous behavior. During training, the GAN generates synthetic normal traffic, creating a diverse set of normal data to train the CNN and help it generalize better to variations in network traffic. In experiments, the hybrid CNN-GAN model demonstrates superior performance in detecting network anomalies compared to traditional methods. It exhibits a high detection rate while minimizing false positives, making it a promising tool for enhancing network security using MATLAB software. The proposed approach contributes to the ongoing efforts to safeguard critical network infrastructures against evolving cyber threats by harnessing the power of AI-driven anomaly detection.
 
Keywords—Generative Adversarial Network (GAN), Convolutional Neural Network (CNN), anomaly detection, cyber threats, network traffic data

Cite: Vuda Sreenivasa Rao, R. Balakrishna, Yousef A. Baker El-Ebiary, Puneet Thapar, K. Aanandha Saravanan, and Sanjiv Rao Godla, "AI Driven Anomaly Detection in Network Traffic Using Hybrid CNN-GAN," Journal of Advances in Information Technology, Vol. 15, No. 7, pp. 886-895, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

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