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Netw."],"published-print":{"date-parts":[[2018,2,28]]},"abstract":"<jats:p>Measurements collected in a wireless sensor network (WSN) can be maliciously compromised through several attacks, but anomaly detection algorithms may provide resilience by detecting inconsistencies in the data. Anomaly detection can identify severe threats to WSN applications, provided that there is a sufficient amount of genuine information. This article presents a novel method to calculate an assurance measure for the network by estimating the maximum number of malicious measurements that can be tolerated. In previous work, the resilience of anomaly detection to malicious measurements has been tested only against arbitrary attacks, which are not necessarily sophisticated. The novel method presented here is based on an optimization algorithm, which maximizes the attack\u2019s chance of staying undetected while causing damage to the application, thus seeking the worst-case scenario for the anomaly detection algorithm. The algorithm is tested on a wildfire monitoring WSN to estimate the benefits of anomaly detection on the system\u2019s resilience. The algorithm also returns the measurements that the attacker needs to synthesize, which are studied to highlight the weak spots of anomaly detection. Finally, this article presents a novel methodology that takes in input the degree of resilience required and automatically designs the deployment that satisfies such a requirement.<\/jats:p>","DOI":"10.1145\/3176621","type":"journal-article","created":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T13:36:22Z","timestamp":1517578582000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1750-8826","authenticated-orcid":false,"given":"Vittorio P.","family":"Illiano","sequence":"first","affiliation":[{"name":"Imperial College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Paudice","sequence":"additional","affiliation":[{"name":"Imperial College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis","family":"Mu\u00f1oz-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Imperial College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emil C.","family":"Lupu","sequence":"additional","affiliation":[{"name":"Imperial College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,2]]},"reference":[{"volume-title":"Retrieved","year":"2015","key":"e_1_2_1_1_1"},{"volume-title":"Malicious node detection in wireless sensor networks using weighted trust evaluation","year":"2008","author":"Atakli Idris M.","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1804647.1804650"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2012.031912.110179"},{"volume-title":"Heuristic Algorithms in Global MINLP Solvers","author":"Berthold Timo","key":"e_1_2_1_5_1"},{"volume-title":"Larkey","year":"2007","author":"Bettencourt Lu\u00eds M. 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