Deep Learning-Based Anomaly Detection in Network Traffic for Cyber Threat Identification

An essential aspect of cybersecurity is the continuously growing threat landscape, which necessitates the use of advanced anomaly detection techniques in network data. The traditional approach might often be inadequate when it comes to addressing intricate cyber-security issues. Therefore, it is possible that deep learning approaches might be superior in terms of accuracy and performance. The primary objective of our study is to provide a novel algorithm that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), autoencoders, and GANs to create a comprehensive strategy for detecting anomalies. This technique aims to solve research gaps that have not been previously explored. By using the MTA-KDD'19 dataset, our research enhances precision by achieving a remarkable accuracy rate of 95% in detecting various types of network traffic abnormalities. This discovery not only demonstrated the harmfulness of our deep learning-based approach but also highlighted the effectiveness of these measures in reducing the issue, particularly when faced with diverse threats. This enhances the development of network security procedures.