Detecting Unknown Attacks: Transformer-Based Image Classifiers with an Out-of-Distribution Detector

Traditional attacks such as viruses, trojans, and backdoors remain significant security challenges, especially as systems face both known and emerging threats. Current detection methods often struggle to identify unknown attacks, leaving systems vulnerable. To address this issue, we propose a transformer-based image classifier with an Out-of-Distribution (OOD) detector that uses the Swin Transformer for known attacks and the local outlier factor (LOF) for unknown attacks. Our approach utilizes the Swin Transformer’s attention mechanism to capture intricate attack patterns while LOF identifies outliers indicative of new, unseen threats. Through evaluation, our method achieved a 0.97 accuracy in classifying known attacks and about 0.7 accuracy in detecting unknown attacks, demonstrating its potential to significantly improve system security and deepen our understanding of traditional attack behaviors.