{"id":"https://openalex.org/W7213874989","doi":"https://doi.org/10.48550/arxiv.2609.21717","title":"A Framework to Quantify the Probability of Future Cyber Loss Events","display_name":"A Framework to Quantify the Probability of Future Cyber Loss Events","publication_year":2026,"publication_date":"2026-09-18","ids":{"openalex":"https://openalex.org/W7213874989","doi":"https://doi.org/10.48550/arxiv.2609.21717"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.21717","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.21717","pdf_url":"https://arxiv.org/pdf/2609.21717","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.21717","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5152350227","display_name":"Siem Peters","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peters, Siem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5074288180","display_name":"Martin Eian","orcid":"https://orcid.org/0009-0004-7461-3202","observed_orcids":["https://orcid.org/0009-0004-7461-3202"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eian, Martin","raw_affiliation_strings":["Oslo , Norway"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oslo , Norway","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10734","display_name":"Information and Cyber Security","score":0.9355000257492065,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10734","display_name":"Information and Cyber Security","score":0.9355000257492065,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.05770000070333481,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.0013000000035390258,"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/cyber-risk-quantification","display_name":"cyber risk quantification","score":0.7760000228881836},{"id":"https://openalex.org/keywords/probabilistic-risk-modeling","display_name":"probabilistic risk modeling","score":0.6959999799728394},{"id":"https://openalex.org/keywords/hierarchical-aggregation","display_name":"hierarchical aggregation","score":0.7250000238418579},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.9269999861717224},{"id":"https://openalex.org/keywords/risk-management","display_name":"risk management","score":0.41499999165534973}],"concepts":[{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6777999997138977},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6212000250816345},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6191999912261963},{"id":"https://openalex.org/C26834552","wikidata":"https://www.wikidata.org/wiki/Q3275846","display_name":"Analyser","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C11489865","wikidata":"https://www.wikidata.org/wiki/Q944806","display_name":"Operational risk","level":3,"score":0.4724999964237213},{"id":"https://openalex.org/C183121708","wikidata":"https://www.wikidata.org/wiki/Q209867","display_name":"Telemetry","level":2,"score":0.46219998598098755},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C193519340","wikidata":"https://www.wikidata.org/wiki/Q891179","display_name":"Data loss","level":2,"score":0.4153999984264374},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.41119998693466187},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3546999990940094},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.336899995803833},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3303000032901764},{"id":"https://openalex.org/C73722673","wikidata":"https://www.wikidata.org/wiki/Q1082833","display_name":"Probabilistic risk assessment","level":3,"score":0.32739999890327454},{"id":"https://openalex.org/C165609540","wikidata":"https://www.wikidata.org/wiki/Q1172486","display_name":"Data breach","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2526000142097473},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.21717","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.21717","pdf_url":"https://arxiv.org/pdf/2609.21717","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.21717","doi":"https://doi.org/10.48550/arxiv.2609.21717","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.21717","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.21717","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.21717","pdf_url":"https://arxiv.org/pdf/2609.21717","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7213874989.pdf","grobid_xml":"https://content.openalex.org/works/W7213874989.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W390686421","https://openalex.org/W617789114","https://openalex.org/W1947393375","https://openalex.org/W1987875779","https://openalex.org/W2122066903","https://openalex.org/W2295598076","https://openalex.org/W2492688925","https://openalex.org/W2525445790","https://openalex.org/W2727136354","https://openalex.org/W2767011015","https://openalex.org/W2793448306","https://openalex.org/W2794421626","https://openalex.org/W2904754882","https://openalex.org/W2949676527","https://openalex.org/W2983895647","https://openalex.org/W4285128587","https://openalex.org/W4313327949","https://openalex.org/W4410708145","https://openalex.org/W4412844373","https://openalex.org/W4414261846","https://openalex.org/W4417034170"],"related_works":["https://openalex.org/W6911996650","https://openalex.org/W4414713297","https://openalex.org/W4367845296","https://openalex.org/W4415145184","https://openalex.org/W7164801808","https://openalex.org/W4206617568","https://openalex.org/W4309196558","https://openalex.org/W4285037642","https://openalex.org/W7217484388","https://openalex.org/W4415653974"],"abstract_inverted_index":{"Cybersecurity":[0],"risk":[1,79,168],"quantification":[2],"remains":[3],"challenging":[4],"due":[5],"to":[6],"limited":[7],"operational":[8,54,142],"data":[9],"and":[10,57,75,89,130,157],"difficulties":[11],"in":[12],"quantifying":[13],"Loss":[14,22,38],"Event":[15,23,39],"Frequency":[16,24],"(LEF).":[17],"This":[18,68],"paper":[19],"introduces":[20],"the":[21,82,90,114,123],"Security":[25],"Analyser":[26],"(LEFSA),":[27],"a":[28,70,119,161],"probabilistic":[29,154],"framework":[30,94],"that":[31,141],"reformulates":[32],"LEF":[33],"estimation":[34,80],"as":[35,160],"machine-level":[36,50,66,155],"Cyber":[37],"(CLE)":[40],"prediction":[41],"combined":[42],"with":[43,118],"hierarchical":[44,158],"infrastructure-level":[45],"aggregation.":[46],"LEFSA":[47],"estimates":[48],"calibrated":[49],"CLE":[51,151],"probabilities":[52],"from":[53,104],"cybersecurity":[55,143],"telemetry":[56,103,144],"aggregates":[58],"them":[59],"across":[60,135],"infrastructure":[61],"layers":[62],"while":[63],"accounting":[64],"for":[65,72,110,149,164],"dependencies.":[67],"provides":[69],"foundation":[71,163],"scalable,":[73],"explainable,":[74],"operationally":[76],"applicable":[77],"cyber":[78,167],"at":[81],"level":[83],"of":[84,128],"machines,":[85],"services,":[86],"business":[87],"processes,":[88],"entire":[91],"organization.":[92],"The":[93,138],"was":[95],"evaluated":[96],"using":[97,107],"proprietary":[98],"Managed":[99],"Detection":[100],"&":[101],"Response":[102],"23":[105],"organizations":[106],"Microsoft":[108],"Defender":[109],"Endpoint.":[111],"XGBoost":[112],"achieved":[113],"strongest":[115],"predictive":[116,147],"performance,":[117],"mean":[120],"area":[121],"under":[122],"receiver":[124],"operating":[125],"characteristic":[126],"curve":[127],"0.90":[129],"consistently":[131],"low":[132],"calibration":[133],"error":[134],"evaluation":[136],"periods.":[137],"results":[139],"demonstrate":[140],"contains":[145],"substantial":[146],"information":[148],"future":[150],"occurrence,":[152],"supporting":[153],"modeling":[156],"aggregation":[159],"promising":[162],"quantitative,":[165],"data-driven":[166],"management.":[169]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-22T00:00:00"}
