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

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.