Abstract
Western-Electric are one of the earliest, and widely used, anomaly detection rules. In this paper we describe an adaptive scenario using these rules and show how a malicious player can optimally fabricate data to deceive the algorithm to enlarge the standard deviation of the data while avoiding being detected.
Keywords
- Western Electric Rules
- Adaptive Version
- Anomaly Detection
- Malicious Players
- Central Limit Theory
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This work was not supported by any organization.
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I’d like to thank the anonymous referees for their helpful remarks, which helped me to improve this paper.
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Margalit, O. (2018). Brief Announcement: Adversarial Evasion of an Adaptive Version of Western Electric Rules. In: Dinur, I., Dolev, S., Lodha, S. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2018. Lecture Notes in Computer Science(), vol 10879. Springer, Cham. https://doi.org/10.1007/978-3-319-94147-9_22
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DOI: https://doi.org/10.1007/978-3-319-94147-9_22
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