SAGEGAN: Style-Based Anomaly Detection with Gaussian Embeddings using Generative Adversarial Networks

Malware evolves faster than rule-based and signature-driven detection pipelines. This paper presents SAGEGAN, a benign-only trained malware anomaly detection framework that converts portable executable files into compact three-channel images and models benign structure through style-conditioned adve