{"id":"https://openalex.org/W7225655935","doi":"https://doi.org/10.63180/jjic.thestap.2027.1.7","title":"Secure-Aggregation Federated Graph Attention Networks with SHAP-Based Edge Attribution for Advanced Persistent Threat Detection","display_name":"Secure-Aggregation Federated Graph Attention Networks with SHAP-Based Edge Attribution for Advanced Persistent Threat Detection","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225655935","doi":"https://doi.org/10.63180/jjic.thestap.2027.1.7"},"language":"en","primary_location":{"id":"doi:10.63180/jjic.thestap.2027.1.7","doi":"https://doi.org/10.63180/jjic.thestap.2027.1.7","is_oa":true,"landing_page_url":"https://doi.org/10.63180/jjic.thestap.2027.1.7","pdf_url":"https://thestap.blob.core.windows.net/jjic/articles/published/1350440515919.pdf","source":{"id":"https://openalex.org/S5407049533","display_name":"Jordanian Journal of Informatics and Computing","issn_l":"3080-6828","issn":["3080-6828"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jordanian Journal of Informatics and Computing","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://thestap.blob.core.windows.net/jjic/articles/published/1350440515919.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161169441","display_name":"Ahmed Fakhri Abdullah","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210128557","display_name":"Ministry of Higher Education and Scientific Research","ror":"https://ror.org/03m5ehy13","country_code":"IQ","type":"government","lineage":["https://openalex.org/I4210128557"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Ahmed Fakhri Abdullah","raw_affiliation_strings":["Ministry of Higher Education and Scientific Research , 10011 , Baghdad , Iraq"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ministry of Higher Education and Scientific Research , 10011 , Baghdad , Iraq","institution_ids":["https://openalex.org/I4210128557"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5099426662","display_name":"Ahmed Sedeeq Baker Al-Doori","orcid":"https://orcid.org/0000-0003-4564-7492","observed_orcids":["https://orcid.org/0000-0003-4564-7492"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed Sedeeq Baker Al-Doori","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2027","issue":"1","first_page":"132","last_page":"132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9077000021934509,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9077000021934509,"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":0.08250000327825546,"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":0.001500000013038516,"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/advanced-persistent-threats","display_name":"advanced persistent threats","score":0.9049999713897705},{"id":"https://openalex.org/keywords/federated-learning","display_name":"federated learning","score":0.7960000038146973},{"id":"https://openalex.org/keywords/secure-aggregation","display_name":"secure aggregation","score":0.9629999995231628},{"id":"https://openalex.org/keywords/graph-attention-networks","display_name":"graph attention networks","score":0.8399999737739563},{"id":"https://openalex.org/keywords/shap-attribution","display_name":"SHAP feature attribution","score":0.6930000185966492},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.8560000061988831},{"id":"https://openalex.org/keywords/provenance-graphs","display_name":"provenance graphs","score":0.7110000252723694},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"anomaly detection","score":0.718999981880188},{"id":"https://openalex.org/keywords/privacy-preserving-machine-learning","display_name":"privacy-preserving machine learning","score":0.7279999852180481},{"id":"https://openalex.org/keywords/incident-investigation","display_name":"incident investigation","score":0.550000011920929}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8335000276565552},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6193000078201294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46950000524520874},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.44699999690055847},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.4174000024795532},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3781999945640564},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.373199999332428},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.37290000915527344},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3310000002384186},{"id":"https://openalex.org/C2992317946","wikidata":"https://www.wikidata.org/wiki/Q712144","display_name":"De facto","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3012999892234802},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.29499998688697815},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27129998803138733},{"id":"https://openalex.org/C123403432","wikidata":"https://www.wikidata.org/wiki/Q654068","display_name":"Visibility","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.63180/jjic.thestap.2027.1.7","doi":"https://doi.org/10.63180/jjic.thestap.2027.1.7","is_oa":true,"landing_page_url":"https://doi.org/10.63180/jjic.thestap.2027.1.7","pdf_url":"https://thestap.blob.core.windows.net/jjic/articles/published/1350440515919.pdf","source":{"id":"https://openalex.org/S5407049533","display_name":"Jordanian Journal of Informatics and Computing","issn_l":"3080-6828","issn":["3080-6828"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jordanian Journal of Informatics and Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.63180/jjic.thestap.2027.1.7","doi":"https://doi.org/10.63180/jjic.thestap.2027.1.7","is_oa":true,"landing_page_url":"https://doi.org/10.63180/jjic.thestap.2027.1.7","pdf_url":"https://thestap.blob.core.windows.net/jjic/articles/published/1350440515919.pdf","source":{"id":"https://openalex.org/S5407049533","display_name":"Jordanian Journal of Informatics and Computing","issn_l":"3080-6828","issn":["3080-6828"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jordanian Journal of Informatics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225655935.pdf","grobid_xml":"https://content.openalex.org/works/W7225655935.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W2807021761","https://openalex.org/W2903957940","https://openalex.org/W2907492528","https://openalex.org/W2964333026","https://openalex.org/W2981336898","https://openalex.org/W2995022099","https://openalex.org/W3013310637","https://openalex.org/W3152893301","https://openalex.org/W3154573290","https://openalex.org/W3156989090","https://openalex.org/W3197292945","https://openalex.org/W3199485401","https://openalex.org/W3212079419","https://openalex.org/W4205228770","https://openalex.org/W4205684821","https://openalex.org/W4251194840","https://openalex.org/W4281686206","https://openalex.org/W4293581802","https://openalex.org/W4311703141","https://openalex.org/W4384948624","https://openalex.org/W4387849006","https://openalex.org/W4388593493","https://openalex.org/W4389060243","https://openalex.org/W4394717695","https://openalex.org/W4400655193","https://openalex.org/W4408716097","https://openalex.org/W4412049236","https://openalex.org/W7133200245","https://openalex.org/W7133202939","https://openalex.org/W7155499371","https://openalex.org/W7155511203","https://openalex.org/W7155978883","https://openalex.org/W7159950817","https://openalex.org/W7163997689","https://openalex.org/W7167828415","https://openalex.org/W7169761283","https://openalex.org/W7171663234","https://openalex.org/W7203895959","https://openalex.org/W7223234360","https://openalex.org/W7225378888","https://openalex.org/W7225394494","https://openalex.org/W7225400657","https://openalex.org/W7225423942"],"related_works":["https://openalex.org/W4388470350","https://openalex.org/W7168282396","https://openalex.org/W4399912498","https://openalex.org/W7127139337","https://openalex.org/W7172392709","https://openalex.org/W7133606742","https://openalex.org/W7127075055","https://openalex.org/W3094151218","https://openalex.org/W7154581081","https://openalex.org/W4312362177"],"abstract_inverted_index":{"Advanced":[0],"Persistent":[1],"Threats":[2],"(APTs)":[3],"pose":[4],"significant":[5],"cybersecurity":[6],"challenges":[7],"due":[8],"to":[9,32,84,106,128,216,236,285,317],"their":[10,280],"stealthy,":[11],"prolonged":[12],"activities":[13,87,247,256],"and":[14,35,44,56,79,110,118,135,157,188,190,200,204,228,269,305,322],"the":[15,147,155,179,193,264,272,292,295],"limited":[16,37],"visibility":[17],"of":[18,132,186,198,244,266,271,297],"malicious":[19,255],"behaviour":[20],"in":[21],"host":[22],"provenance":[23,65],"data.":[24],"Existing":[25],"detection":[26,63,83,174,213,243],"approaches":[27],"often":[28],"require":[29],"centralized":[30],"access":[31],"sensitive":[33],"logs":[34],"provide":[36,129],"interpretability,":[38],"raising":[39],"concerns":[40],"about":[41],"data":[42,92],"confidentiality":[43],"analyst":[45],"trust.":[46],"To":[47,95],"address":[48],"these":[49],"challenges,":[50],"this":[51],"study":[52],"proposes":[53],"a":[54,98,161,249],"privacy-preserving":[55],"explainable":[57,306],"federated":[58,80,164,218,300],"learning":[59],"framework":[60,69,143],"for":[61,202,308],"APT":[62,310],"using":[64,146],"graphs.":[66],"The":[67,142,207,260],"proposed":[68,208],"integrates":[70],"edge-typed":[71],"Graph":[72],"Attention":[73],"Networks":[74],"(GATv2),":[75],"self-supervised":[76],"masked-feature":[77],"learning,":[78,301,304],"prototype-based":[81],"anomaly":[82],"identify":[85],"suspicious":[86],"without":[88],"requiring":[89],"labeled":[90],"attack":[91,232,246],"during":[93,139],"training.":[94],"preserve":[96],"confidentiality,":[97],"dropout-resilient,":[99],"Bonawitz-style":[100],"secure":[101,209,299],"aggregation":[102,210],"protocol":[103],"is":[104,126,144,283],"employed":[105],"protect":[107],"model":[108],"updates":[109],"shared":[111],"statistics,":[112],"including":[113],"normalization":[114],"parameters,":[115],"clustering":[116],"information,":[117],"calibration":[119],"distributions.":[120],"Furthermore,":[121],"edge-level":[122],"Shapley":[123,267],"value":[124],"attribution":[125],"incorporated":[127],"interpretable":[130],"explanations":[131],"detected":[133],"anomalies":[134],"support":[136],"security":[137,324],"analysts":[138],"incident":[140],"investigation.":[141],"evaluated":[145],"DARPA":[148],"Transparent":[149],"Computing":[150],"Engagement":[151],"3":[152],"benchmark,":[153],"specifically":[154],"THEIA":[156,203],"CADETS":[158],"datasets,":[159],"under":[160],"simulated":[162],"four-client":[163],"setting.":[165],"Experimental":[166],"results":[167],"across":[168],"three":[169],"random":[170],"seeds":[171],"demonstrate":[172,294],"strong":[173],"performance,":[175],"achieving":[176],"Area":[177,191],"Under":[178,192],"Receiver":[180],"Operating":[181],"Characteristic":[182],"Curve":[183,195],"(AUROC)":[184],"values":[185,197],"0.995":[187],"0.971":[189],"Precision\u2013Recall":[194],"(AUPRC)":[196],"0.870":[199],"0.561":[201],"CADETS,":[205],"respectively.":[206],"mechanism":[211],"maintains":[212],"performance":[214],"comparable":[215,284],"conventional":[217],"averaging,":[219],"introduces":[220],"approximately":[221],"0.1%":[222],"additional":[223],"training":[224],"time":[225],"on":[226],"THEIA,":[227],"reduces":[229],"cross-round":[230],"client-linkage":[231],"accuracy":[233],"from":[234],"85.6%":[235],"chance-level":[237],"performance.":[238],"Behaviour-level":[239],"analysis":[240,262],"demonstrates":[241],"complete":[242],"scan-like":[245],"at":[248],"calibrated":[250],"95%":[251],"threshold,":[252],"while":[253,312],"other":[254],"exhibit":[257],"threshold-dependent":[258],"recall.":[259],"explainability":[261],"confirms":[263],"completeness":[265],"attributions":[268],"convergence":[270],"highest-ranked":[273],"contributing":[274],"edges":[275],"within":[276],"25":[277],"permutations,":[278],"although":[279],"empirical":[281],"fidelity":[282],"less":[286],"computationally":[287],"expensive":[288],"explanation":[289,320],"methods.":[290],"Overall,":[291],"findings":[293],"potential":[296],"combining":[298],"provenance-graph":[302],"representation":[303],"AI":[307],"confidentiality-preserving":[309],"detection,":[311],"highlighting":[313],"important":[314],"limitations":[315],"related":[316],"threshold":[318],"sensitivity,":[319],"fidelity,":[321],"semi-honest":[323],"assumptions.":[325]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}
