{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:06:05Z","timestamp":1769717165644,"version":"3.49.0"},"reference-count":33,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>Wireless sensor nodes (WSN) combine sensing and communication capabilities in the smallest sensor network component. Sensor nodes have basic networking capabilities, such as wireless connection with other nodes, data storage, and a microcontroller to do basic processing. The intrusion detection problem is well analyzed and there exist numerous techniques to solve this issue but suffer will poor intrusion detection accuracy and a higher false alarm ratio. To overcome this challenge, a novel Intrusion Detection via Salp Swarm Optimization based Deep Learning Algorithm (ID-SODA) has been proposed which classifies intrusion node and non-intrusion node. The proposed ID-SODA technique uses the k-means clustering algorithm to perform clustering. The Salp Swarm Optimization (SSO) technique takes into residual energy, distance, and cost while choosing the cluster head selection (CHS). The CHS is given the input to a multi-head convolutional neural network (MHCNN), which will classify into intrusion node and non-intrusion node. The performance analysis of the suggested ID-SODA is evaluated based on the parameters like accuracy, precision, F1 score, detection rate, recall, false alarm rate, and false negative rate. The suggested ID-SODA achieves an accuracy range of 98.95%. The result shows that the suggested ID-SODA improves the overall accuracy better than 6.56%, 2.94%, and 2.95% in SMOTE, SLGBM, and GWOSVM-IDS respectively.<\/jats:p>","DOI":"10.3233\/jifs-231756","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T11:19:31Z","timestamp":1688123971000},"page":"6897-6909","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Blocking intrusion logic using optimized multi-head convolution in wireless sensor network"],"prefix":"10.1177","volume":"45","author":[{"given":"S.","family":"Prabhu","sequence":"first","affiliation":[{"name":"S.A. Engineering College","place":["India"]}]},{"given":"E.A.","family":"Mary Anita","sequence":"additional","affiliation":[{"name":"Christ University","place":["India"]}]},{"given":"D.","family":"Mohanageetha","sequence":"additional","affiliation":[{"name":"Sri Krishna College of Engineering and Technology","place":["India"]}]}],"member":"179","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22031070"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-189756"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.11591\/ijeecs.v21.i1.pp516-523"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114603"},{"issue":"2","key":"e_1_3_1_6_2","first-page":"45","article-title":"Intrusion detection with wireless sensor network (WSN) internet of things","volume":"13","author":"Hendrawan A.","year":"2021","unstructured":"HendrawanA., DaruA.F. and HirzanA.M., Intrusion detection with wireless sensor network (WSN) internet of things. Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 13(2) (2021), 45\u201348.","journal-title":"Journal of Telecommunication, Electronic and Computer Engineering (JTEC)"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.3103\/S1060992X20030029"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-08288-4"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"BegliM. DerakhshanF. and KarimipourH. August. A layered intrusion detection system for critical infrastructure using machine learning. In 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE) (2019) (pp. 120\u2013124). IEEE.","DOI":"10.1109\/SEGE.2019.8859950"},{"key":"e_1_3_1_10_2","first-page":"102448","article-title":"An effective genetic algorithm-based feature selection method for intrusion detection systems","volume":"110","author":"Halim Z.","year":"2021","unstructured":"HalimZ., YousafM.N., WaqasM., SulaimanM., AbbasG., HussainM., AhmadI. and HanifM., An effective genetic algorithm-based feature selection method for intrusion detection systems. Security & Security 110 (2021), 102448.","journal-title":"Security & Security"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2021.0120437"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-com.2019.0172"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05017-0"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2970973"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-019-01980-1"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10723-022-09619-w"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2022.06.011"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2022.104489"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1002\/dac.5076"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-04955-z"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-022-01070-9"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-2181-4"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2895334"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-04678-1"},{"key":"e_1_3_1_25_2","first-page":"120","article-title":"A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: A data mining concept","volume":"23","author":"Borkar G.M.","year":"2019","unstructured":"BorkarG.M., PatilL.H., DalgadeD. and HutkeA., A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: A data mining concept. puting: Informatics and Systems 23 (2019), 120\u2013135.","journal-title":"puting: Informatics and Systems"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19010203"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3024219"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2021.3077946"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12083-020-01025-x"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02751-z"},{"key":"e_1_3_1_31_2","unstructured":"GowdhamanV. and DhanapalR. an intrusion detection system for wireless sensor networks using deep neural network. Soft Computing (2021) 1\u20139."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3073413"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02228-z"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22041407"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-231756","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-231756","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-231756","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T08:58:13Z","timestamp":1769677093000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-231756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,10,4]]}},"alternative-id":["10.3233\/JIFS-231756"],"URL":"https:\/\/doi.org\/10.3233\/jifs-231756","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,30]]}}}