Interactive System-wise Anomaly Detection
Guanchu Wang, Ninghao Liu, Daochen Zha, Xia Hu

TL;DR
This paper introduces InterSAD, an interactive system-wise anomaly detection method that models systems as Markov decision processes and uses reinforcement learning to identify anomalous systems through real-time interactions.
Contribution
The paper proposes a novel end-to-end framework combining system embedding, policy learning, and a stabilization training method for system-wise anomaly detection.
Findings
Outperforms state-of-the-art baselines in benchmark tests
Effective in identifying anomalous robotic systems
Detects user data poisoning in recommendation models
Abstract
Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications. However, it is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. Appropriate interactions are needed to interact with the systems and identify those with abnormal responses. Detecting system-wise anomalies is a challenging task due to several reasons including: how to formally define the system-wise anomaly detection problem; how to find the effective activation signal for interacting with systems to progressively collect the data and learn the detector; how to guarantee stable training in such a non-stationary scenario with real-time interactions? To address the challenges, we propose InterSAD (Interactive System-wise Anomaly…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Network Security and Intrusion Detection · Data Stream Mining Techniques
