Unsupervised Feature Learning with Generative Adversarial Networks for Anomaly Detection in Cybersecurity

Abstract

When it comes to cybersecurity, anomaly detection is vital for finding harmful actions and possible dangers to computer networks. Conventional methods of anomaly identification frequently use established criteria or characteristics that have been manually designed, neither of which are foolproof against cyberattacks' nuanced and intricate patterns. We provide a fresh method for detecting anomalies that makes use of generative adversarial networks (GANs) for unsupervised feature learning. Successfully learning a latent representation of the underlying data distribution has allowed GANs to produce realistic data samples. Unsupervisedly, we develop meaningful representations of network traffic data by using the discriminative power of GANs. In order to train a GAN to differentiate between typical and abnormal network traffic, we teach it to look for characteristics that represent the structure of typical data and how to change its behaviour when abnormalities are present. Then, we spot out-of-the-ordinary activity in live network traffic streams by using the features we've learnt. We test our method using a freely accessible dataset of network traffic and show that it can accurately and efficiently detect different kinds of cyber assaults with few false positives. Our findings provide credence to the idea that GAN-based unsupervised feature learning might supplement current approaches to strengthening network security posture and significantly improve cybersecurity anomaly detection.