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.
By Kamil Faber, Mateusz Smendowski, Roberto Corizzo
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.
By Manu Nandan, TJ Jaymes, Michael Brautbar, Edward Raff
arXiv:2510. 26307v3 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience.
By Laura Jiang, Reza Ryan, Qian Li, Nasim Ferdosian
arXiv:2512. 22179v3 Announce Type: replace Abstract: Detecting previously unseen attacks remains a major challenge for machine learning-based intrusion detection systems.
By Rajeeb Thapa Chhetri, Saurab Thapa, Avinash Kumar, Zhixiong Chen
The paper introduces a deep positive‑unlabeled anomaly detection framework that combines positive‑unlabeled learning with deep models such as autoencoders and deep support vector data descriptions. It addresses the issue of contaminated unlabeled data by approximating anomaly scores for normal data using both unlabeled and labeled anomaly samples, allowing training without labeled normal data. The authors provide a theoretical generalization error bound and demonstrate improved detection performance over existing methods on several datasets.
By Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka
arXiv:2607. 18479v1 Announce Type: new Abstract: Malicious anomalous activity detection is a fundamental challenge for cyber security systems.
By Dorianis M. Perez, Maksim E. Eren, Bryan E. Kaiser