Online Anomaly Detection

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9 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 35

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ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage
May 9, 2026Wenbo Gao, Songbai Tan, Zhongan Wang +6Online Anomaly Detection Financial Fraud Detection
Harbin Institute of Technology, Shenzhen · Shenzhen University · Zhejiang University +4
Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation
May 9, 2026Yutszyuk Wong, Wentai Wu, Yuen-Ying Yeung +1Online Anomaly Detection Anomaly Localization
Jinan University · Guangzhou, China · South China University of Technology
EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems
May 1, 2026Luan Pham, Victor Nicolet, Joey Dodds +2Online Anomaly Detection Anomaly Localization
RMIT University, Australia · Amazon Web Services, United States
Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation
Apr 16, 2026Jiaqi Zhu, Shaofeng Cai, Jie Chen +3Online Anomaly Detection Concept Drift
School of Automation and the National Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China · School of Computing, National University of Singapore, Singapore 117417 · Harbin Institute of Technology, Harbin 150001, China +2
Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions
Sep 17, 2025Michel A. YoussefCost-Sensitive Learning Probability Calibration
Independent Researcher · Independent Researcher, Lebanon

May 9, 2026, cs.LGJ/K move · Enter open · S save

ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage

Wenbo Gao, Songbai Tan, Zhongan Wang, Fei Shen+5

Harbin Institute of Technology, Shenzhen · Shenzhen University · Zhejiang University+4

Online Anomaly Detection Financial Fraud Detection Memory-Augmented Agents Anomaly Detection

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Smartphone scams are increasingly prevalent and typically manifest as multi-stage, cross-application processes with gradually emerging intent. Effective intervention thus requires anticipating scams before the intent becomes explicit. This is inherently challenging, as decisions must rely on partial trajectories with temporally distributed evidence. In this paper, we propose \textbf{ORACLE} Online Reasoning for Anticipating Cross-temporal Latent thrEats, the first agentic framework for early scam anticipation from \textit{streaming app-usage} trajectories. To support this setting, we curate a real-world long-horizon benchmark of streaming app-usage trajectories, covering 12 scam types, spanning extended periods (15 days on average), involving diverse applications (95 apps), and interleaving normal and scam behaviors. To address fragmented evidence, we introduce a self-evolving context manager that adaptively consolidates entity-centric interactions over time, enabling more effective reconstruction of cross-temporal evidence from partial observations. To enhance sensitivity to latent early-stage signals, we propose an on-policy self-distillation scheme in which a teacher model, conditioned on summarized anti-scam reflections and clues by skills, supervises a student model without access to such reflections. This scheme thereby distills evidence-informed knowledge and improves recognition of emerging fraud patterns from partial trajectories. Experiments show that \method{} consistently improves early scam anticipation, yielding timely warnings while reducing false alerts in realistic streaming scenarios.

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Latest papers 35

ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage

Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation

EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems

Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation

Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions