{"id":"https://openalex.org/W4400233772","doi":"https://doi.org/10.1109/noms59830.2024.10575087","title":"IT Intrusion Detection Using Statistical Learning and Testbed Measurements","display_name":"IT Intrusion Detection Using Statistical Learning and Testbed Measurements","publication_year":2024,"publication_date":"2024-05-06","ids":{"openalex":"https://openalex.org/W4400233772","doi":"https://doi.org/10.1109/noms59830.2024.10575087"},"language":"en","primary_location":{"id":"doi:10.1109/noms59830.2024.10575087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/noms59830.2024.10575087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"NOMS 2024-2024 IEEE Network Operations and Management Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003853801","display_name":"Xiaoxuan Wang","orcid":"https://orcid.org/0000-0002-2414-3108"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Xiaoxuan Wang","raw_affiliation_strings":["KTH Royal Institute of Technology,Dept. of Computer Science,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Dept. of Computer Science,Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035040105","display_name":"Rolf Stadler","orcid":"https://orcid.org/0000-0001-6039-8493"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Rolf Stadler","raw_affiliation_strings":["KTH Royal Institute of Technology,Dept. of Computer Science,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Dept. of Computer Science,Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9919000267982483,"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":0.9919000267982483,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9462000131607056,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9329000115394592,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.9210972189903259},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.7385208606719971},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6917026042938232},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3387529253959656},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.16914516687393188}],"concepts":[{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.9210972189903259},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.7385208606719971},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6917026042938232},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3387529253959656},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.16914516687393188}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/noms59830.2024.10575087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/noms59830.2024.10575087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"NOMS 2024-2024 IEEE Network Operations and Management Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1580437619","https://openalex.org/W1811853421","https://openalex.org/W1856750239","https://openalex.org/W1964406293","https://openalex.org/W1975869217","https://openalex.org/W2064675550","https://openalex.org/W2096266510","https://openalex.org/W2105594594","https://openalex.org/W2125838338","https://openalex.org/W2142384583","https://openalex.org/W2152668193","https://openalex.org/W2166855330","https://openalex.org/W2507920413","https://openalex.org/W2516066574","https://openalex.org/W2591712613","https://openalex.org/W2613731841","https://openalex.org/W2621409261","https://openalex.org/W2725713545","https://openalex.org/W2739717417","https://openalex.org/W2755588949","https://openalex.org/W2789828921","https://openalex.org/W2889885925","https://openalex.org/W2908818413","https://openalex.org/W2911964244","https://openalex.org/W2913414826","https://openalex.org/W2946419751","https://openalex.org/W2958285686","https://openalex.org/W2959653735","https://openalex.org/W2982379385","https://openalex.org/W3043097943","https://openalex.org/W3104378004","https://openalex.org/W3133138345","https://openalex.org/W3164999323","https://openalex.org/W4283728389","https://openalex.org/W4383503710","https://openalex.org/W6637096788","https://openalex.org/W6838356584"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2883256816","https://openalex.org/W2171408034","https://openalex.org/W3003320923","https://openalex.org/W2106140982","https://openalex.org/W2152313554","https://openalex.org/W2064303750","https://openalex.org/W1509300825","https://openalex.org/W3092582874"],"abstract_inverted_index":{"We":[0,38],"study":[1,202],"automated":[2],"intrusion":[3,209],"detection":[4,210],"in":[5,139,156],"an":[6,17,28,95,103],"IT":[7,105],"infrastructure,":[8],"specifically":[9],"the":[10,14,19,24,36,80,181,199],"problem":[11],"of":[12,16,21,26,60,64,126,134],"identifying":[13],"start":[15,159],"attack,":[18,22],"type":[20],"and":[23,52,82,151,163,188],"sequence":[25],"actions":[27],"attacker":[29],"takes,":[30],"based":[31],"on":[32,94,180],"continuous":[33],"measurements":[34,117],"from":[35,90,118,204],"infrastructure.":[37,106],"apply":[39],"statistical":[40],"learning":[41],"methods,":[42],"including":[43],"Hidden":[44],"Markov":[45],"Model":[46],"(HMM),":[47],"Long":[48],"Short-Term":[49],"Memory":[50],"(LSTM),":[51],"Random":[53],"Forest":[54],"Classifier":[55],"(RFC)":[56],"to":[57,62,70,78,108,123,130],"map":[58],"sequences":[59,63],"observations":[61],"predicted":[65],"attack":[66,158,161,164],"actions.":[67,165],"In":[68],"contrast":[69],"most":[71],"related":[72],"research,":[73],"we":[74,92,99,146,196,201],"have":[75],"abundant":[76],"data":[77,88,169,191,205],"train":[79],"models":[81],"evaluate":[83],"their":[84],"predictive":[85],"power.":[86],"The":[87],"comes":[89],"traces":[91],"generate":[93],"in-house":[96],"testbed":[97],"where":[98],"run":[100],"attacks":[101],"against":[102],"emulated":[104],"Central":[107],"our":[109],"work":[110],"is":[111,170],"a":[112,119,124,131],"machine-learning":[113],"pipeline":[114],"that":[115,148,198],"maps":[116],"high-dimensional":[120],"observation":[121,135],"space":[122,125],"low":[127],"dimensionality":[128],"or":[129],"small":[132],"set":[133],"symbols.":[136],"Investigating":[137],"intrusions":[138],"offline":[140],"as":[141,143],"well":[142],"online":[144],"scenarios,":[145],"find":[147,197],"both":[149],"HMM":[150],"LSTM":[152,172],"can":[153],"be":[154],"effective":[155,193],"predicting":[157],"time,":[160],"type,":[162],"If":[166],"sufficient":[167],"training":[168,190],"available,":[171],"achieves":[173],"higher":[174],"prediction":[175],"accuracy":[176],"than":[177],"HMM.":[178],"HMM,":[179],"other":[182],"hand,":[183],"requires":[184],"less":[185,189],"computational":[186],"resources":[187],"for":[192],"prediction.":[194],"Also,":[195],"methods":[200],"benefit":[203],"produced":[206],"by":[207],"traditional":[208],"systems":[211],"like":[212],"SNORT.":[213]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}