arXiv Machine Learning July 31, 2026 By Tian-Yi Zhou, Matthew Lau, Jizhou Chen, Wenke Lee, Xiaoming Huo

Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights

Read the original on arXiv Machine Learning →

arXiv:2409. 08521v2 Announce Type: replace-cross Abstract: In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previously observed behaviors.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios

arXiv:2607. 18289v1 Announce Type: cross Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes.

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Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks

arXiv:2606. 28923v1 Announce Type: new Abstract: Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure.

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Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection

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Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection

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We Need to Rethink Benchmarking in Anomaly Detection

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