{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T15:31:08Z","timestamp":1770219068386,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2012,8,7]],"date-time":"2012-08-07T00:00:00Z","timestamp":1344297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.<\/jats:p>","DOI":"10.3390\/s120810834","type":"journal-article","created":{"date-parts":[[2012,8,7]],"date-time":"2012-08-07T10:56:27Z","timestamp":1344336987000},"page":"10834-10850","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Detecting Unknown Attacks in Wireless Sensor Networks That Contain Mobile Nodes"],"prefix":"10.3390","volume":"12","author":[{"given":"Zorana","family":"Bankovi\u0107","sequence":"first","affiliation":[{"name":"Departamento de Ingenier\u00eda Electr\u00f3nica, ETSI Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid, Av. Complutense, 30, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Fraga","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Electr\u00f3nica, ETSI Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid, Av. Complutense, 30, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 M.","family":"Moya","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Electr\u00f3nica, ETSI Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid, Av. Complutense, 30, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Carlos","family":"Vallejo","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Electr\u00f3nica, ETSI Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid, Av. Complutense, 30, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Krontiris, I., Giannetsos, T., and Dimitriou, T. (2008, January 22\u201325). LIDeA: A Distributed Lightweight Intrusion Detection Architecture for Sensor Networks. Istanbul, Turkey.","DOI":"10.1145\/1460877.1460903"},{"key":"ref_2","unstructured":"Hai, T.H., Khan, F.I., and Huh, E. (, January October). Hybrid Intrusion Detection System for Wireless Sensor Networks. San Francisco, CA, USA."},{"key":"ref_3","unstructured":"Onat, I., and Miri, A. (2005, January 14\u201317). A Real-Time Node-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks. 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