{"id":"https://openalex.org/W3136786760","doi":"https://doi.org/10.1109/bigdata50022.2020.9378018","title":"Machine Learning Methods for Anomaly Detection in Industrial Control Systems","display_name":"Machine Learning Methods for Anomaly Detection in Industrial Control Systems","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3136786760","doi":"https://doi.org/10.1109/bigdata50022.2020.9378018","mag":"3136786760"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5085160517","display_name":"Johnathan Tai","orcid":null},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Johnathan Tai","raw_affiliation_strings":["College of Engineering & Computer Science, Syracuse University, Syracuse, NY"],"affiliations":[{"raw_affiliation_string":"College of Engineering & Computer Science, Syracuse University, Syracuse, NY","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075965221","display_name":"Izzat Alsmadi","orcid":"https://orcid.org/0000-0001-7832-5081"},"institutions":[{"id":"https://openalex.org/I1335518801","display_name":"Texas A&M University \u2013 San Antonio","ror":"https://ror.org/0084njv03","country_code":"US","type":"education","lineage":["https://openalex.org/I1335518801"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Izzat Alsmadi","raw_affiliation_strings":["Department of Computing and Cyber Security, Texas A&M, San Antonio, San Antonio, Texas"],"affiliations":[{"raw_affiliation_string":"Department of Computing and Cyber Security, Texas A&M, San Antonio, San Antonio, Texas","institution_ids":["https://openalex.org/I1335518801"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100457273","display_name":"Yunpeng Zhang","orcid":"https://orcid.org/0000-0001-6208-9571"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunpeng Zhang","raw_affiliation_strings":["Department of Information and Logistics Technology, University of Houston, Houston, Texas"],"affiliations":[{"raw_affiliation_string":"Department of Information and Logistics Technology, University of Houston, Houston, Texas","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082932847","display_name":"Fengxiang Qiao","orcid":"https://orcid.org/0000-0001-9074-0288"},"institutions":[{"id":"https://openalex.org/I48205209","display_name":"Texas Southern University","ror":"https://ror.org/05ch0aw77","country_code":"US","type":"education","lineage":["https://openalex.org/I48205209"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fengxiang Qiao","raw_affiliation_strings":["College of Science, Engineering & Technology, Texas Southern University, Houston, Texas"],"affiliations":[{"raw_affiliation_string":"College of Science, Engineering & Technology, Texas Southern University, Houston, Texas","institution_ids":["https://openalex.org/I48205209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5085160517"],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":0.3083,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.63733718,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"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":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991999864578247,"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/T10917","display_name":"Smart Grid Security and Resilience","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.7899307012557983},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7363252639770508},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7139797210693359},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7068561911582947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7029396295547485},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.7003288865089417},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6970418691635132},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6740270256996155},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6723425984382629},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5274760127067566},{"id":"https://openalex.org/keywords/industrial-control-system","display_name":"Industrial control system","score":0.5062074065208435},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.41327065229415894},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.2395561933517456}],"concepts":[{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.7899307012557983},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7363252639770508},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7139797210693359},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7068561911582947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7029396295547485},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.7003288865089417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6970418691635132},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6740270256996155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6723425984382629},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5274760127067566},{"id":"https://openalex.org/C40071531","wikidata":"https://www.wikidata.org/wiki/Q2513962","display_name":"Industrial control system","level":3,"score":0.5062074065208435},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.41327065229415894},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2395561933517456},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1565377632","https://openalex.org/W1993132138","https://openalex.org/W2167710876","https://openalex.org/W2342408547","https://openalex.org/W2586277768","https://openalex.org/W2620038827","https://openalex.org/W2762776925","https://openalex.org/W2787708942","https://openalex.org/W2962883549","https://openalex.org/W2963143631","https://openalex.org/W2963311060","https://openalex.org/W2963389226","https://openalex.org/W2963744840","https://openalex.org/W2964253222","https://openalex.org/W2969330742","https://openalex.org/W2984333088","https://openalex.org/W3005260862","https://openalex.org/W3008497156","https://openalex.org/W3023328046","https://openalex.org/W3029114445","https://openalex.org/W3109365969","https://openalex.org/W4242097849","https://openalex.org/W6729756640","https://openalex.org/W6739868092","https://openalex.org/W6748475379","https://openalex.org/W6766533389"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W4310224730","https://openalex.org/W3094138326","https://openalex.org/W1985505753"],"abstract_inverted_index":{"This":[0],"paper":[1],"examines":[2],"multiple":[3],"machine":[4,29],"learning":[5,30],"models":[6,31,74],"to":[7],"find":[8],"the":[9,51,57,95],"model":[10],"that":[11,21,60,87],"best":[12],"indicates":[13],"anomalous":[14],"activity":[15],"in":[16,81],"an":[17],"industrial":[18,82],"control":[19,83],"system":[20],"is":[22],"under":[23],"a":[24],"software-based":[25],"attack.":[26],"The":[27],"researched":[28],"are":[32],"Random":[33,61,88],"Forest,":[34,62],"Gradient":[35,63],"Boosting":[36,64],"Machine,":[37,65],"Artificial":[38,66],"Neural":[39,43,67],"Network,":[40,68],"and":[41,48,69],"Recurrent":[42],"Network":[44],"classifiers":[45],"built-in":[46],"Python":[47],"tested":[49],"against":[50],"HIL-based":[52],"Augmented":[53],"ICS":[54],"dataset.":[55],"Although":[56],"results":[58],"showed":[59],"Long":[70],"Short-Term":[71],"Memory":[72],"classification":[73],"have":[75],"great":[76],"potential":[77],"for":[78],"anomaly":[79],"detection":[80],"systems,":[84],"we":[85],"found":[86],"Forest":[89],"with":[90],"tuned":[91],"hyperparameters":[92],"slightly":[93],"outperformed":[94],"other":[96],"models.":[97]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
