Detection Latencies of Anomaly Detectors: An Overlooked Perspective ?
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Abstract (arXiv)
The ever-evolving landscape of attacks, coupled with the growing complexity of ICT systems, makes crafting anomaly-based intrusion detectors (ID) and error detectors (ED) a difficult task: they must accurately detect attacks, and they should promptly perform detections. Although improving and comparing the detection capability is the focus of most research works, the timeliness of the detection is less considered and often insufficiently evaluated or discussed. In this paper, we argue the relevance of measuring the temporal latency of attacks and errors, and we propose an evaluation approach for detectors to ensure a pragmatic trade-off between correct and in-time detection. Briefly, the approach relates the false positive rate with the temporal latency of attacks and errors, and this ultimately leads to guidelines for configuring a detector. We apply our approach by evaluating different ED and ID solutions in two industrial cases: i) an embedded railway on-board system that optimizes public mobility, and ii) an edge device for the Industrial Internet of Things. Our results show that considering latency in addition to traditional metrics like the false positive rate, precision, and coverage gives an additional fundamental perspective on the actual performance of the detector and should be considered when assessing and configuring anomaly detectors.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 81 references that are in the map. First posted on arXiv 14 Feb 2024.
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9 of 81 references.
References · 9
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
- 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 199
- 2017LSTM Fully Convolutional Networks for Time Series Classificationcited by 136
- 2019Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detectioncited by 124
- 2018A review on distance based time series classificationcited by 40
- 2021A Survey on Anomaly Detection for Technical Systems using LSTM Networkscited by 27
- 2024ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical Systemcited by 3
- 2023LZR: Identifying Unexpected Internet Servicescited by 2
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