{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T00:29:06Z","timestamp":1777076946841,"version":"3.51.4"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"vor","delay-in-days":148,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006261","name":"Taif University","doi-asserted-by":"publisher","award":["TURSP-2020\/239"],"award-info":[{"award-number":["TURSP-2020\/239"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset where outliers are deleted. In the process of semisupervised clustering, vast prior knowledge is added, and the dataset is clustered according to the principle of graph segmentation. The novelty of the proposed algorithm lies in outlier detection while effectively avoiding the dependence on parameters, thus eliminating the influence of outliers on clustering. This article uses real datasets: lypmphography and glass for simulation purposes. The simulation results show that the algorithm proposed in this paper can effectively detect outliers and has a good clustering effect. Furthermore, the experimentation reveals that the outlier detection\u2010based SCA\u2010SNN algorithm has the best practical effect on the dataset without outliers, clearly validating the clustering performance of the outlier detection\u2010based SCA\u2010SNN algorithm. Furthermore, compared to the other state\u2010of\u2010the\u2010art anomaly detection method, it was revealed that the anomaly detection technology based on outlier mining does not require a training process. Thus, they overcome the current anomaly detection problems caused due to incomplete normal patterns in training samples.<\/jats:p>","DOI":"10.1155\/2021\/5558860","type":"journal-article","created":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T20:06:16Z","timestamp":1622318776000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3126-0641","authenticated-orcid":false,"given":"Yajing","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4270-0999","authenticated-orcid":false,"given":"Juan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4990-5252","authenticated-orcid":false,"given":"Ashutosh","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7676-9014","authenticated-orcid":false,"given":"Pradeep Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0732-1478","authenticated-orcid":false,"given":"Gurjot Singh","family":"Gaba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6019-7245","authenticated-orcid":false,"given":"Mehedi","family":"Masud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2417-4374","authenticated-orcid":false,"given":"Mohammed","family":"Baz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3047662"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2021.04.021"},{"key":"e_1_2_9_3_2","volume-title":"Intrusion detection","author":"Bace R. G.","year":"2000"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.SP.800-94"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.12.024"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2019.106527"},{"key":"e_1_2_9_7_2","unstructured":"Day\u0131o\u011fluB. Use of Passive Network Mapping to Enhange Network Intrusion Detection [M.S. thesis] 2001 University Library Middle East Technical University Turkey."},{"key":"e_1_2_9_8_2","volume-title":"Data Mining Techniques for (Network) Intrusion Detection Systems","author":"Lappas T.","year":"2007"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114150"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-07835-3"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2008.12.005"},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"SinghV.andPuthranS. Intrusion detection system using data mining a review 2016 International Conference on Global Trends in Signal Processing Information Computing and Communication (ICGTSPICC) 2016 Jalgaon India 587\u2013592 https:\/\/doi.org\/10.1109\/ICGTSPICC.2016.7955369 2-s2.0-85025176093.","DOI":"10.1109\/ICGTSPICC.2016.7955369"},{"key":"e_1_2_9_13_2","first-page":"181","article-title":"Design hybrid method for intrusion detection using ensemble cluster classification and som network","volume":"2","author":"Rathore D.","year":"2019","journal-title":"International Journal of Advanced Computer Research"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12083-021-01162-x"},{"key":"e_1_2_9_15_2","article-title":"Lightweight and anonymity-preserving user authentication scheme for IoT-based healthcare","author":"Masud M.","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2799854"},{"key":"e_1_2_9_17_2","first-page":"70","article-title":"Anomaly-based intrusion detection using deep neural networks","volume":"12","author":"Farahnakian F.","year":"2018","journal-title":"International Journal of Digital Content Technology and its Applications"},{"key":"e_1_2_9_18_2","first-page":"6","article-title":"Intrusion detection method based on deep neural network","volume":"46","author":"Qian T.","year":"2018","journal-title":"Huazhong Keji Daxue Xuebao"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.5815\/ijcnis.2018.11.05"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"MakarfiA. U. RabieK. M. KaiwartyaO. LiX. andKharelR. Physical layer security in vehicular networks with reconfigurable intelligent surfaces 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) 2020 Antwerp Belgium 1\u20136.","DOI":"10.1109\/VTC2020-Spring48590.2020.9128438"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2018916270"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2934632"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-018-2517-0"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2848106"},{"key":"e_1_2_9_25_2","first-page":"979","article-title":"Detection of network protection security vulnerability intrusion based on data mining","volume":"21","author":"Zhang J.","year":"2019","journal-title":"International Journal of Network Security"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.4018\/JCIT.2019100102"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10766-017-0537-7"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2929919"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2962829"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2863036"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2995133"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900214"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2017.2745110"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2917299"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03765-w"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-021-01094-y"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.2174\/1573405617666210224115722"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-08272-y"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1108\/ijicc-10-2020-0142"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1049\/cim2.12019"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.3390\/su13063405"},{"key":"e_1_2_9_42_2","first-page":"73","article-title":"A framework for pre-computated multi-constrained quickest QoS path algorithm","volume":"9","author":"Sharma A.","year":"2017","journal-title":"Journal of Telecommunication, Electronic and Computer Engineering (JTEC)"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2960367"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.2174\/2352096512666190215141938"}],"container-title":["Journal of Sensors"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2021\/5558860.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2021\/5558860.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5558860","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:37:02Z","timestamp":1722904622000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5558860"}},"subtitle":[],"editor":[{"given":"Omprakash","family":"Kaiwartya","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5558860"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5558860","archive":["Portico"],"relation":{},"ISSN":["1687-725X","1687-7268"],"issn-type":[{"value":"1687-725X","type":"print"},{"value":"1687-7268","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-02-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5558860"}}