{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T09:09:58Z","timestamp":1785316198439,"version":"3.55.0"},"reference-count":143,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation (NRF)","award":["BK21 FOUR"],"award-info":[{"award-number":["BK21 FOUR"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML\u2019s potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment.<\/jats:p>","DOI":"10.3390\/s24196377","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T11:08:47Z","timestamp":1727780927000},"page":"6377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["The Intersection of Machine Learning and Wireless Sensor Network Security for Cyber-Attack Detection: A Detailed Analysis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4646-763X","authenticated-orcid":false,"given":"Tahesin Samira","family":"Delwar","sequence":"first","affiliation":[{"name":"Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Unal","family":"Aras","sequence":"additional","affiliation":[{"name":"Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayak","family":"Mukhopadhyay","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay","family":"Kumar","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ujwala","family":"Kshirsagar","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5251-6100","authenticated-orcid":false,"given":"Yangwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mangal","family":"Singh","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jee-Youl","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"01010","DOI":"10.1051\/itmconf\/20224301010","article-title":"Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches","volume":"Volume 43","author":"Eljakani","year":"2022","journal-title":"ITM Web of Conferences"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Intanagonwiwat, C., Govindan, R., and Estrin, D. 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