| 1. |
- Gualandi, Gabriele, et al.
(author)
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Worst-Case Impact Assessment of Multi-Alarm Stealth Attacks Against Control Systems with CUSUM-Based Anomaly Detection
- 2023
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In: 2023 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS, ACSOS. - 9798350337440 ; , s. 117-126
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Conference paper (other academic/artistic)abstract
- Manipulating sensor data can deceive cyber-physical systems (CPSs), leading to hazardous conditions in physical plants. An Anomaly Detection System (ADS) like CUSUM detects ongoing attacks by comparing sensor signals with those generated by a model. However, physics-based methods are threshold-based, which can result in both false positives and undetectable attacks. This can lead to undetected attacks impacting the system state and potentially causing large deviations from the desired behavior. In this paper, we introduce a metric called transparency that uniquely quantifies the effectiveness of an ADS in terms of its ability to prevent state deviation. While existing research focuses on designing optimal zero-alarm stealth attacks, we address the challenge of detecting more sophisticated multi-alarm attacks that generate alarms at a rate comparable to the system noise. Through our analysis, we identify the conditions that require the inclusion of multi-alarm scenarios in worst-case impact assessments. We also propose an optimization problem designed to identify multi-alarm attacks by relaxing the constraints of a zero-alarm attack problem. Our findings reveal that multi-alarm attacks can cause a more significant state deviation than zero-alarm attacks, emphasizing their critical importance in the security analysis of control systems.
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| 2. |
- Topfer, Michal, et al.
(author)
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Online ML Self-adaptation in Face of Traps
- 2023
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In: Proceedings - 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2023. - : IEEE. - 9798350337440 ; , s. 57-66
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Conference paper (peer-reviewed)abstract
- Online machine learning (ML) is often used in selfadaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report its limitations. Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties - traps - that, to our knowledge, are not discussed enough in the community. In this paper, we report our experience with these traps. Specifically, we discuss several traps that relate to the specification and online training of the ML-based estimators, their impact on selfadaptation, and the approach used to evaluate the estimators. Our overview of these traps provides a list of lessons learned, which can serve as guidance for other researchers and practitioners when applying online ML for self-adaptation.
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