Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples
Kyle Stein, Arash Mahyari, Guillermo Francia III, Eman El-Sheikh

TL;DR
This paper introduces a self-supervised transformer-based method for malware detection in network traffic, capable of generalizing to unseen threats with minimal labeled data, outperforming traditional supervised approaches.
Contribution
It proposes a novel self-supervised transformer model trained on unlabeled network data, enabling effective few-shot malware detection and improved generalization to unseen attacks.
Findings
Achieves up to 94.76% accuracy on UNSW-NB15 dataset.
Attains 83.25% accuracy on CIC-IoT23 dataset.
Demonstrates strong generalization to unseen malware with limited labeled samples.
Abstract
As networks continue to expand and become more interconnected, the need for novel malware detection methods becomes more pronounced. Traditional security measures are increasingly inadequate against the sophistication of modern cyber attacks. Deep Packet Inspection (DPI) has been pivotal in enhancing network security, offering an in-depth analysis of network traffic that surpasses conventional monitoring techniques. DPI not only examines the metadata of network packets, but also dives into the actual content being carried within the packet payloads, providing a comprehensive view of the data flowing through networks. While the integration of advanced deep learning techniques with DPI has introduced modern methodologies into malware detection and network traffic classification, state-of-the-art supervised learning approaches are limited by their reliance on large amounts of annotated…
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Taxonomy
TopicsImage and Object Detection Techniques · Advanced Measurement and Detection Methods · Industrial Vision Systems and Defect Detection
