{"id":"https://openalex.org/W4206517916","doi":"https://doi.org/10.1109/ai-csp52968.2021.9671122","title":"A New Intelligent Jamming Attacks Detection using FCM clustering technique Based on Data Mining for Wireless Communication","display_name":"A New Intelligent Jamming Attacks Detection using FCM clustering technique Based on Data Mining for Wireless Communication","publication_year":2021,"publication_date":"2021-11-20","ids":{"openalex":"https://openalex.org/W4206517916","doi":"https://doi.org/10.1109/ai-csp52968.2021.9671122"},"language":"en","primary_location":{"id":"doi:10.1109/ai-csp52968.2021.9671122","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ai-csp52968.2021.9671122","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011088568","display_name":"Ahmed Moumena","orcid":"https://orcid.org/0000-0001-5385-3793"},"institutions":[{"id":"https://openalex.org/I4210164694","display_name":"Hassiba Benbouali University of Chlef","ror":"https://ror.org/04yymzm67","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210164694"]}],"countries":["DZ"],"is_corresponding":true,"raw_author_name":"Ahmed Moumena","raw_affiliation_strings":["Hassiba Benbouali University of Chlef, Algeria"],"affiliations":[{"raw_affiliation_string":"Hassiba Benbouali University of Chlef, Algeria","institution_ids":["https://openalex.org/I4210164694"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5011088568"],"corresponding_institution_ids":["https://openalex.org/I4210164694"],"apc_list":null,"apc_paid":null,"fwci":0.1751,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49196651,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/jamming","display_name":"Jamming","score":0.7667948603630066},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7281025052070618},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6899404525756836},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6697294116020203},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.6616060733795166},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.47142356634140015},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46150946617126465},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4462969899177551},{"id":"https://openalex.org/keywords/nyquist\u2013shannon-sampling-theorem","display_name":"Nyquist\u2013Shannon sampling theorem","score":0.43609851598739624},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.41331180930137634},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36634331941604614},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.23191240429878235},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1558041274547577},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09011250734329224}],"concepts":[{"id":"https://openalex.org/C2779079576","wikidata":"https://www.wikidata.org/wiki/Q17092823","display_name":"Jamming","level":2,"score":0.7667948603630066},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7281025052070618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899404525756836},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6697294116020203},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.6616060733795166},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.47142356634140015},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46150946617126465},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4462969899177551},{"id":"https://openalex.org/C288623","wikidata":"https://www.wikidata.org/wiki/Q679800","display_name":"Nyquist\u2013Shannon sampling theorem","level":2,"score":0.43609851598739624},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.41331180930137634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36634331941604614},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.23191240429878235},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1558041274547577},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09011250734329224},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ai-csp52968.2021.9671122","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ai-csp52968.2021.9671122","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1572298868","https://openalex.org/W1941358455","https://openalex.org/W1990368529","https://openalex.org/W1992419399","https://openalex.org/W1993784401","https://openalex.org/W2011962436","https://openalex.org/W2037673368","https://openalex.org/W2109000886","https://openalex.org/W2114129195","https://openalex.org/W2134120396","https://openalex.org/W2149596792","https://openalex.org/W2164813427","https://openalex.org/W2166806817","https://openalex.org/W2289951411","https://openalex.org/W2466322703","https://openalex.org/W2912056303","https://openalex.org/W4250955649"],"related_works":["https://openalex.org/W4200319726","https://openalex.org/W1984793747","https://openalex.org/W4312547492","https://openalex.org/W3197729047","https://openalex.org/W1624160917","https://openalex.org/W1987034598","https://openalex.org/W2370486284","https://openalex.org/W2143847498","https://openalex.org/W2354671671","https://openalex.org/W2963685604"],"abstract_inverted_index":{"This":[0,81],"work":[1,90],"proposes":[2],"a":[3],"new":[4,82],"intelligent":[5,42,83,109],"jamming":[6],"attacks":[7],"detector":[8,45,86],"Fuzzy":[9],"C":[10],"Means":[11],"(FCM)":[12],"clustering":[13],"combined":[14],"with":[15],"MWC":[16,32],"via":[17,33],"cooperative":[18,34,54,123],"sensing":[19],"based":[20,92],"sub-Nyquist":[21],"sampling":[22,26],"theory.":[23],"The":[24,99],"compressed":[25],"(CS)":[27],"data":[28,84],"matrix":[29],"obtained":[30],"after":[31],"is":[35,91],"considered":[36],"as":[37],"the":[38,41,51,71,79,94,102,112,118],"input":[39],"of":[40,53,75,96,101,111,115,120,122],"FCM":[43,97],"abnormal":[44,113],"in":[46,78,88,117],"fusion":[47],"center":[48],"(FC).":[49],"In":[50],"presence":[52,72,121],"pulse":[55],"jammers":[56],"and":[57,73],"assuming":[58],"Additive":[59],"White":[60],"Gaussian":[61],"noise":[62],"(AWGN),":[63],"we":[64],"propose":[65],"two":[66],"hypotheses":[67],"to":[68],"differentiate":[69],"between":[70],"absence":[74],"malicious":[76],"attackers":[77],"system.":[80],"mining":[85],"proposed":[87,103],"this":[89],"on":[93],"characteristics":[95],"clustering.":[98],"performance":[100],"system":[104],"shows":[105],"good":[106],"results":[107],"about":[108],"detection":[110],"behavior":[114],"patterns":[116],"case":[119],"jammers.":[124]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
