{"id":"https://openalex.org/W7163984172","doi":"https://doi.org/10.1145/3748522.3779878","title":"SimGuard: Towards Resilience of Community-based Intrusion Detection","display_name":"SimGuard: Towards Resilience of Community-based Intrusion Detection","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7163984172","doi":"https://doi.org/10.1145/3748522.3779878"},"language":null,"primary_location":{"id":"doi:10.1145/3748522.3779878","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3779878","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3748522.3779878","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083976858","display_name":"Philipp Eichhammer","orcid":"https://orcid.org/0000-0003-0604-4437"},"institutions":[{"id":"https://openalex.org/I186354981","display_name":"University of Passau","ror":"https://ror.org/05ydjnb78","country_code":"DE","type":"education","lineage":["https://openalex.org/I186354981"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Philipp Eichhammer","raw_affiliation_strings":["University of Passau, Passau, Germany"],"raw_orcid":"https://orcid.org/0000-0003-0604-4437","affiliations":[{"raw_affiliation_string":"University of Passau, Passau, Germany","institution_ids":["https://openalex.org/I186354981"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085415375","display_name":"Hans P. Reiser","orcid":"https://orcid.org/0000-0002-2815-5747"},"institutions":[{"id":"https://openalex.org/I123185442","display_name":"Reykjav\u00edk University","ror":"https://ror.org/05d2kyx68","country_code":"IS","type":"education","lineage":["https://openalex.org/I123185442"]}],"countries":["IS"],"is_corresponding":false,"raw_author_name":"Hans P. Reiser","raw_affiliation_strings":["Reykjavik University, Reykjavik, Iceland"],"raw_orcid":"https://orcid.org/0000-0002-2815-5747","affiliations":[{"raw_affiliation_string":"Reykjavik University, Reykjavik, Iceland","institution_ids":["https://openalex.org/I123185442"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083976858"],"corresponding_institution_ids":["https://openalex.org/I186354981"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.96109282,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"385","last_page":"394"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.8601999878883362,"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"}},"topics":[{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.8601999878883362,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.03220000118017197,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.018300000578165054,"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/intrusion-detection-system","display_name":"Intrusion detection system","score":0.7716000080108643},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6589000225067139},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.5709999799728394},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5478000044822693},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.41280001401901245},{"id":"https://openalex.org/keywords/intrusion","display_name":"Intrusion","score":0.4124000072479248},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.37040001153945923},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.351500004529953}],"concepts":[{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.7716000080108643},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7243000268936157},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6589000225067139},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.5709999799728394},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5478000044822693},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.413100004196167},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.41280001401901245},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.367900013923645},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.351500004529953},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.32749998569488525},{"id":"https://openalex.org/C137176749","wikidata":"https://www.wikidata.org/wiki/Q4105337","display_name":"Psychological resilience","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C137524506","wikidata":"https://www.wikidata.org/wiki/Q2247688","display_name":"Anomaly-based intrusion detection system","level":3,"score":0.3167000114917755},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C29852176","wikidata":"https://www.wikidata.org/wiki/Q373338","display_name":"Critical infrastructure","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C27061796","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion prevention system","level":3,"score":0.29589998722076416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29010000824928284},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2700999975204468}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748522.3779878","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3779878","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3748522.3779878","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3779878","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.664810061454773,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1772700132","https://openalex.org/W2619824656","https://openalex.org/W2898017895","https://openalex.org/W2982426954","https://openalex.org/W3030742901","https://openalex.org/W3140367595","https://openalex.org/W4214537185","https://openalex.org/W4220851938","https://openalex.org/W4224284455","https://openalex.org/W4290948380","https://openalex.org/W4317795078","https://openalex.org/W4391724779","https://openalex.org/W4396680620","https://openalex.org/W4403770987","https://openalex.org/W4404612323"],"related_works":[],"abstract_inverted_index":{"The":[0],"rise":[1],"of":[2,26,59,78,125,151,160],"sizable":[3],"and":[4,46,93,108],"complex":[5],"IoT":[6],"infrastructures":[7,85],"increased":[8],"research":[9],"interest":[10],"in":[11,44,57,90,110,131,158,182],"Collaborative":[12],"Intrusion":[13],"Detection":[14],"Systems":[15],"(CIDSs).":[16],"Therefore,":[17],"federated":[18,134],"learning":[19,135],"(FL)":[20,136],"mechanisms":[21],"offer":[22],"a":[23,54,65,81],"privacy-preserving":[24],"method":[25,66],"collaboration,":[27],"which,":[28],"however,":[29],"is":[30,118],"subject":[31],"to":[32],"poisoning":[33,48,146],"attacks":[34,147],"that":[35,141],"degrade":[36],"model":[37],"accuracy.":[38],"While":[39],"there":[40],"are":[41],"solutions":[42],"effective":[43],"detecting":[45,68],"mitigating":[47],"attacks,":[49],"their":[50,91,96],"methodology":[51],"always":[52],"assumes":[53],"static":[55],"infrastructure":[56],"terms":[58],"benign":[60,88],"behavior.":[61],"This":[62,116],"work":[63],"presents":[64],"for":[67],"malicious":[69],"data":[70,74],"injection":[71],"into":[72],"training":[73],"under":[75],"the":[76,123,175],"assumption":[77],"concept":[79,114,124,156],"drift,":[80],"phenomenon":[82],"where":[83],"real-world":[84,183],"experience":[86],"gradual,":[87],"changes":[89,107,109],"behavior":[92,111],"structure":[94],"throughout":[95],"life":[97],"cycle.":[98],"Our":[99,138],"approach":[100,143],"can":[101,144,167],"successfully":[102],"differentiate":[103],"between":[104,127],"maliciously":[105],"provoked":[106],"induced":[112],"by":[113,121],"drift.":[115],"feature":[117],"made":[119],"possible":[120],"leveraging":[122],"similarity":[126],"anomaly":[128],"detection":[129,164],"models":[130],"conjunction":[132],"with":[133,148],"technology.":[137],"results":[139],"demonstrate":[140],"our":[142,163],"detect":[145],"an":[149],"accuracy":[150],"97%,":[152],"while":[153,173],"correctly":[154],"identifying":[155],"drift":[157],"98%":[159],"cases.":[161],"With":[162],"methods,":[165],"we":[166],"maintain":[168],"high":[169],"true":[170],"positive":[171,177],"rates":[172],"keeping":[174],"false":[176],"rate":[178],"low":[179],"over":[180],"time":[181],"infrastructures.":[184]},"counts_by_year":[],"updated_date":"2026-06-10T14:10:52.464848","created_date":"2026-06-10T00:00:00"}