{"id":"https://openalex.org/W7133337750","doi":"https://doi.org/10.48550/arxiv.2603.00859","title":"AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learning","display_name":"AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learning","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133337750","doi":"https://doi.org/10.48550/arxiv.2603.00859"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00859","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00859","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.00859","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119876253","display_name":"Oluseyi Olukola","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olukola, Oluseyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102764912","display_name":"Nick Rahimi","orcid":"https://orcid.org/0000-0002-1964-1794"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahimi, Nick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"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":false,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.8551999926567078,"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":0.8551999926567078,"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":0.0471000000834465,"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":0.016200000420212746,"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/intrusion-detection-system","display_name":"Intrusion detection system","score":0.7531999945640564},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6629999876022339},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5764999985694885},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5577999949455261},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5142999887466431},{"id":"https://openalex.org/keywords/anomaly-based-intrusion-detection-system","display_name":"Anomaly-based intrusion detection system","score":0.4885999858379364},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4675999879837036},{"id":"https://openalex.org/keywords/backdoor","display_name":"Backdoor","score":0.4221999943256378},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.40380001068115234}],"concepts":[{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.7531999945640564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7398999929428101},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6629999876022339},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6013000011444092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5888000130653381},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5577999949455261},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5142999887466431},{"id":"https://openalex.org/C137524506","wikidata":"https://www.wikidata.org/wiki/Q2247688","display_name":"Anomaly-based intrusion detection system","level":3,"score":0.4885999858379364},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46540001034736633},{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C2776973144","wikidata":"https://www.wikidata.org/wiki/Q6880649","display_name":"Misuse detection","level":4,"score":0.2985999882221222},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00859","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00859","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.00859","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00859","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4393581449985504,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1],"based":[2],"network":[3],"intrusion":[4,87,199],"detection":[5,29,50,74,80,88,200],"systems":[6],"are":[7],"vulnerable":[8],"to":[9,132,147,192],"adversarial":[10,69,194],"attacks":[11,124],"that":[12,47,91,163,180],"degrade":[13],"classification":[14,106],"performance":[15],"under":[16,98],"both":[17],"gradient-based":[18],"and":[19,61,104,112,151,171],"distribution":[20],"shift":[21],"threat":[22],"models.":[23],"Existing":[24],"defenses":[25],"typically":[26],"apply":[27],"uniform":[28],"strategies,":[30],"which":[31],"may":[32,172],"not":[33,153],"account":[34],"for":[35],"heterogeneous":[36],"attack":[37,70,139],"characteristics.":[38],"This":[39],"paper":[40],"proposes":[41],"an":[42],"attack-aware":[43],"multi-stage":[44],"defense":[45,164],"framework":[46],"learns":[48],"attack-specific":[49,181],"strategies":[51],"through":[52],"a":[53,77,85,137,155,189],"weighted":[54],"combination":[55],"of":[56],"ensemble":[57],"disagreement,":[58],"predictive":[59],"uncertainty,":[60],"distributional":[62],"anomaly":[63],"signals.":[64],"Empirical":[65],"analysis":[66],"across":[67],"seven":[68],"types":[71],"reveals":[72],"distinct":[73],"signatures,":[75],"enabling":[76],"two-stage":[78],"adaptive":[79,122,149],"mechanism.":[81],"Experimental":[82],"evaluation":[83,144],"on":[84,167],"benchmark":[86],"dataset":[89],"indicates":[90],"the":[92,99,129],"proposed":[93],"system":[94,130],"attains":[95],"94.2%":[96],"area":[97],"receiver":[100],"operating":[101],"characteristic":[102],"curve":[103],"improves":[105],"accuracy":[107,135],"by":[108,114],"4.5":[109],"percentage":[110],"points":[111,116],"F1-score":[113],"9.0":[115],"over":[117],"adversarially":[118],"trained":[119],"ensembles.":[120],"Under":[121],"white-box":[123],"with":[125,136,174,184],"full":[126],"architectural":[127],"knowledge,":[128],"appears":[131],"maintain":[133],"94.4%":[134],"4.2%":[138],"success":[140],"rate,":[141],"though":[142],"this":[143],"is":[145],"limited":[146],"two":[148],"variants":[150],"does":[152],"constitute":[154],"formal":[156],"robustness":[157,195],"guarantee.":[158],"Cross-dataset":[159],"validation":[160],"further":[161],"suggests":[162],"effectiveness":[165],"depends":[166],"baseline":[168],"classifier":[169],"competence":[170],"vary":[173],"feature":[175],"dimensionality.":[176],"These":[177],"results":[178],"suggest":[179],"optimization":[182],"combined":[183],"multi-signal":[185],"integration":[186],"can":[187],"provide":[188],"practical":[190],"approach":[191],"improving":[193],"in":[196],"machine":[197],"learning-based":[198],"systems.":[201]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-04T00:00:00"}
