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.
- 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.
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
Western Electric Company: Statistical Quality Control Handbook. Western Electric Co, Indianapolis (1956)
Nelson, L.S.: The Shewhart control chart tests for special causes. J. Qual. Technol. 16, 237–239 (1984)
Romano, M., Kapelan, Z., Savic, D.: Automated detection of pipe bursts and other events in water distribution systems. American Society of Civil Engineers (2012)
Lovell, D.P., Fellows, M., Marchetti, F., Christiansen, J., Elhajouji, A., Hashimoto, K., Kasamoto, S., Li, Y., Masayasu, O., Moore, M.M., Schuler, M., Smith, R., Stankowski, L.F., Tanaka, J., Tanir, J.Y., Thybaud, V., Van Goethem, F., Whitwell, J.: Analysis of negative historical control group data from the in vitro micronucleus assay using TK6 cells. Mutation Res./Genet. Toxicol. Environ. Mutagen. 825, 40–50 (2018)
I’d like to thank the anonymous referees for their helpful remarks, which helped me to improve this paper.
Editors and Affiliations
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
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
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94146-2
Online ISBN: 978-3-319-94147-9