{"id":"https://openalex.org/W4387969890","doi":"https://doi.org/10.1109/cns59707.2023.10288656","title":"Self-similarity based network anomaly detection for industrial control systems","display_name":"Self-similarity based network anomaly detection for industrial control systems","publication_year":2023,"publication_date":"2023-10-02","ids":{"openalex":"https://openalex.org/W4387969890","doi":"https://doi.org/10.1109/cns59707.2023.10288656"},"language":"en","primary_location":{"id":"doi:10.1109/cns59707.2023.10288656","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cns59707.2023.10288656","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Conference on Communications and Network Security (CNS)","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/A5055955332","display_name":"Bryan Martin","orcid":null},"institutions":[{"id":"https://openalex.org/I35364215","display_name":"Naval Postgraduate School","ror":"https://ror.org/033yfkj90","country_code":"US","type":"education","lineage":["https://openalex.org/I1330347796","https://openalex.org/I3130687028","https://openalex.org/I35364215"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bryan Martin","raw_affiliation_strings":["Naval Postgraduate School,Dept. of Electrical &#x0026; Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Naval Postgraduate School,Dept. of Electrical &#x0026; Computer Engineering","institution_ids":["https://openalex.org/I35364215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036861959","display_name":"Chad Bollmann","orcid":"https://orcid.org/0000-0001-8812-9391"},"institutions":[{"id":"https://openalex.org/I35364215","display_name":"Naval Postgraduate School","ror":"https://ror.org/033yfkj90","country_code":"US","type":"education","lineage":["https://openalex.org/I1330347796","https://openalex.org/I3130687028","https://openalex.org/I35364215"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chad A. Bollmann","raw_affiliation_strings":["Naval Postgraduate School,Dept. of Electrical &#x0026; Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Naval Postgraduate School,Dept. of Electrical &#x0026; Computer Engineering","institution_ids":["https://openalex.org/I35364215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I35364215"],"apc_list":null,"apc_paid":null,"fwci":0.3755,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62240542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10138","display_name":"Network Traffic and Congestion Control","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/self-similarity","display_name":"Self-similarity","score":0.8225753307342529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.656502366065979},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.5910087823867798},{"id":"https://openalex.org/keywords/ethernet","display_name":"Ethernet","score":0.5822966694831848},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.550858199596405},{"id":"https://openalex.org/keywords/hurst-exponent","display_name":"Hurst exponent","score":0.5318014025688171},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.5257328748703003},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4764159917831421},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.44536417722702026},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43543541431427},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3570955693721771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26675742864608765},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1513761281967163},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10337606072425842},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07431432604789734}],"concepts":[{"id":"https://openalex.org/C119453123","wikidata":"https://www.wikidata.org/wiki/Q262372","display_name":"Self-similarity","level":2,"score":0.8225753307342529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.656502366065979},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.5910087823867798},{"id":"https://openalex.org/C172173386","wikidata":"https://www.wikidata.org/wiki/Q79984","display_name":"Ethernet","level":2,"score":0.5822966694831848},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.550858199596405},{"id":"https://openalex.org/C96835011","wikidata":"https://www.wikidata.org/wiki/Q1638718","display_name":"Hurst exponent","level":2,"score":0.5318014025688171},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.5257328748703003},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4764159917831421},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.44536417722702026},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43543541431427},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3570955693721771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26675742864608765},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1513761281967163},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10337606072425842},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07431432604789734},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cns59707.2023.10288656","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cns59707.2023.10288656","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Conference on Communications and Network Security (CNS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W267165598","https://openalex.org/W1100760050","https://openalex.org/W1538014383","https://openalex.org/W1663605321","https://openalex.org/W1781459780","https://openalex.org/W1821640959","https://openalex.org/W1870161617","https://openalex.org/W2019853444","https://openalex.org/W2064205969","https://openalex.org/W2084611853","https://openalex.org/W2085027889","https://openalex.org/W2127002947","https://openalex.org/W2138888775","https://openalex.org/W2407991977","https://openalex.org/W2540989615","https://openalex.org/W2898916818","https://openalex.org/W2908140179","https://openalex.org/W2912726523","https://openalex.org/W2986736896","https://openalex.org/W3028246429","https://openalex.org/W3088478754","https://openalex.org/W3158335281","https://openalex.org/W4243420524","https://openalex.org/W6638809352","https://openalex.org/W6713723272"],"related_works":["https://openalex.org/W2377886499","https://openalex.org/W1602918169","https://openalex.org/W2127392574","https://openalex.org/W1506543445","https://openalex.org/W2573791844","https://openalex.org/W2149756718","https://openalex.org/W113696316","https://openalex.org/W3047008523","https://openalex.org/W3021897514","https://openalex.org/W3119734647"],"abstract_inverted_index":{"Network":[0],"traffic":[1,43,59,75,78,89],"has":[2,26,51],"been":[3],"shown":[4,55],"to":[5,32,53,123,134],"be":[6,54,118],"self-similar":[7,92],"across":[8],"a":[9,102,121],"wide":[10],"range":[11],"of":[12,23,73,95,105,130],"protocols,":[13],"including":[14],"Ethernet,":[15],"Wi-Fi,":[16],"and":[17,133],"cellular":[18],"traffic.":[19],"However,":[20],"the":[21,24,41,57,71,87,96,99,114,127,139],"composition":[22],"Internet":[25],"grown":[27],"since":[28],"these":[29],"initial":[30],"findings":[31],"include":[33],"machine-to-machine":[34],"(M2M)":[35],"traffic,":[36],"which":[37],"behaves":[38],"differently":[39],"than":[40],"human-generated":[42],"previously":[44],"analyzed.":[45],"In":[46],"this":[47],"changing":[48],"landscape,":[49],"it":[50],"yet":[52],"if":[56],"M2M":[58,74,88],"generated":[60],"in":[61,126,138],"industrial":[62],"control":[63],"systems":[64],"(ICS)":[65],"is":[66],"self-similar.":[67],"This":[68],"paper":[69],"investigates":[70],"self-similarity":[72],"using":[76,110],"network":[77],"from":[79],"three":[80],"publicly":[81],"available":[82],"datasets.":[83],"We":[84],"find":[85],"that":[86,113],"was":[90],"not":[91],"for":[93],"two":[94],"datasets,":[97],"while":[98],"third":[100],"showed":[101],"low":[103],"degree":[104],"self-similarity.":[106],"Furthermore,":[107],"we":[108],"demonstrate":[109],"physical":[111],"data":[112],"Hurst":[115],"parameter":[116],"can":[117],"used":[119],"as":[120],"metric":[122],"observe":[124],"changes":[125],"system":[128],"configuration":[129],"an":[131],"ICS":[132],"detect":[135],"anomalous":[136],"activity":[137],"network.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
