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This can be accomplished by looking at traffic network activity, but it takes a lot of work. The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies. In terms of feature selection, NIDS is far more effective. This is accurate since anomaly identification uses a number of time-consuming features. Because of this, the feature selection method influences how long it takes to analyze movement patterns and how clear it is. The goal of the study is to provide NIDS with an attribute selection approach. PSO has been used for that purpose. The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network, allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised. In the research, datasets were produced utilising both a network infrastructure and a simulation network. Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment. Additionally, a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot, has been built and communication packets are being recorded using the Wireshark tool. To train several machine learning models, all the datasets that were gathered\u2014created datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle\u2014were employed. Additionally, PSO, which is an optimization method, has been used with these ML algorithms for feature selection. In the research, KNN, decision trees, and ANN have all been combined with PSO for a specific case study. And it was found demonstrated the classification methods PSO\u2009+\u2009ANN outperformed PSO\u2009+\u2009KNN and PSO\u2009+\u2009DT in this case study.<\/jats:p>","DOI":"10.1186\/s42400-023-00161-0","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T02:01:46Z","timestamp":1696298506000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Intrusion detection systems for wireless sensor networks using computational intelligence techniques"],"prefix":"10.1186","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5084-7555","authenticated-orcid":false,"given":"Vaishnavi","family":"Sivagaminathan","sequence":"first","affiliation":[]},{"given":"Manmohan","family":"Sharma","sequence":"additional","affiliation":[]},{"given":"Santosh Kumar","family":"Henge","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"issue":"201","key":"161_CR1","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.procs.2022.03.029","volume":"1","author":"EE Abdallah","year":"2022","unstructured":"Abdallah EE, Otoom AF (2022) Intrusion detection systems using supervised machine learning techniques: a survey. 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And the work shown in the paper is original.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"27"}}