IEEE

Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques

Abstract: Anomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats. This study investigates the ...

AI exposes attacks traditional detection methods can’t see

The more urgent issue is what our detection systems still cannot see — and side-channel attacks are making that gap visible.
GitHub

[WACV2025] AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2

This is the official code to reproduce the experiments in the paper AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2, accepted at IEEE/CVF Winter Conference on Applications of ...
GitHub

Leveraging RAG-Enhanced Large Language Model for Semi-Supervised Log Anomaly Detection (ISSRE-2024)

Log-based anomaly detection is critical in monitoring the operations of information systems and in the realtime reporting of system failures. Utilizing deep learning-based log anomaly detection ...
IEEE

Reading Between the Signals: A Comprehensive Tutorial on Harnessing Physical Side-Channels for Anomaly Detection

Abstract: Side-channel analysis (SCA), which involves analyzing physical emissions from devices to infer sensitive information, was traditionally regarded as a technique for enabling cryptographic ...