{"id":"https://openalex.org/W7128530725","doi":"https://doi.org/10.48550/arxiv.2602.09015","title":"CIC-Trap4Phish: A Unified Multi-Format Dataset for Phishing and Quishing Attachment Detection","display_name":"CIC-Trap4Phish: A Unified Multi-Format Dataset for Phishing and Quishing Attachment Detection","publication_year":2026,"publication_date":"2026-02-09","ids":{"openalex":"https://openalex.org/W7128530725","doi":"https://doi.org/10.48550/arxiv.2602.09015"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.09015","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050849301","display_name":"Fatemeh Nejati","orcid":"https://orcid.org/0000-0002-8991-7442"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nejati, Fatemeh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125522346","display_name":"Mahdi Rabbani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rabbani, Mahdi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Eskandarian, Morteza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eskandarian, Morteza","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Mirani, Mansur","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mirani, Mansur","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Piya, Gunjan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Piya, Gunjan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125481512","display_name":"Ali A. Ghorbani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Opushnyev, Igor","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ghorbani, Ali A.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghorbani, Ali A.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052766937","display_name":"Sajjad Dadkhah","orcid":"https://orcid.org/0000-0002-5582-0255"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dadkhah, Sajjad","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5050849301"],"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/T11644","display_name":"Spam and Phishing Detection","score":0.9846000075340271,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9846000075340271,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.00430000014603138,"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/T12519","display_name":"Cybercrime and Law Enforcement Studies","score":0.004100000020116568,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/phishing","display_name":"Phishing","score":0.8679999709129333},{"id":"https://openalex.org/keywords/malware","display_name":"Malware","score":0.8202000260353088},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5753999948501587},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5134000182151794},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.46299999952316284},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.439300000667572},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.39149999618530273},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3885999917984009},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3765000104904175}],"concepts":[{"id":"https://openalex.org/C83860907","wikidata":"https://www.wikidata.org/wiki/Q135005","display_name":"Phishing","level":3,"score":0.8679999709129333},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.8202000260353088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896999716758728},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5753999948501587},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5134000182151794},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.40459999442100525},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39149999618530273},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3772999942302704},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37369999289512634},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C84525096","wikidata":"https://www.wikidata.org/wiki/Q3506050","display_name":"Cryptovirology","level":3,"score":0.3668999969959259},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3091000020503998},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.30160000920295715},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C137287247","wikidata":"https://www.wikidata.org/wiki/Q1329550","display_name":"Static program analysis","level":4,"score":0.2847999930381775},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.25949999690055847},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.09015","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.09015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.09015","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.09015","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.700238049030304,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Phishing":[0],"attacks":[1],"represents":[2],"one":[3],"of":[4,43,105,163,193,255],"the":[5,113,156,179],"primary":[6],"attack":[7],"methods":[8,241],"which":[9],"is":[10,103],"used":[11,138],"by":[12,213,246],"cyber":[13],"attackers.":[14],"In":[15],"many":[16],"cases,":[17],"attackers":[18,56,81],"use":[19],"deceptive":[20],"emails":[21],"along":[22],"with":[23],"malicious":[24,44,67,87,130],"attachments":[25,46],"to":[26,83,89,171,181],"trick":[27],"users":[28],"into":[29],"giving":[30],"away":[31],"sensitive":[32],"information":[33],"or":[34,66,183],"installing":[35],"malware":[36,65],"while":[37],"compromising":[38],"entire":[39],"systems.":[40],"The":[41,208],"flexibility":[42],"email":[45,75],"makes":[47],"them":[48],"stand":[49],"out":[50],"as":[51,57,64],"a":[52,79,125,161,191],"preferred":[53],"vector":[54],"for":[55,204],"they":[58],"can":[59],"embed":[60],"harmful":[61],"content":[62,88],"such":[63],"URLs":[68,257],"inside":[69],"standard":[70],"document":[71],"formats.":[72,233],"Although":[73],"phishing":[74,140,237],"defenses":[76],"have":[77],"improved":[78],"lot,":[80],"continue":[82],"abuse":[84],"attachments,":[85],"enabling":[86],"bypass":[90],"security":[91],"measures.":[92],"Moreover,":[93],"another":[94],"challenge":[95],"that":[96,111],"researches":[97],"face":[98],"in":[99,139],"training":[100],"advance":[101],"models,":[102,218],"lack":[104],"an":[106],"unified":[107],"and":[108,131,151,175,196,223,252],"comprehensive":[109],"dataset":[110,127],"covers":[112],"most":[114],"prevalent":[115],"data":[116],"types.":[117],"To":[118],"address":[119],"this":[120],"gap,":[121],"we":[122],"generated":[123],"CIC-Trap4Phish,":[124],"multi-format":[126],"containing":[128],"both":[129],"benign":[132],"samples":[133],"across":[134,232],"five":[135],"categories":[136],"commonly":[137],"campaigns:":[141],"Microsoft":[142],"Word":[143],"documents,":[144],"Excel":[145],"spreadsheets,":[146],"PDF":[147],"files,":[148],"HTML":[149],"pages,":[150],"QR":[152,235],"code":[153],"images.":[154],"For":[155,234],"first":[157],"four":[158],"file":[159,206],"types,":[160],"set":[162],"execution-free":[164],"static":[165],"feature":[166,197,202],"pipeline":[167],"was":[168,188],"proposed,":[169],"designed":[170],"capture":[172],"structural,":[173],"lexical,":[174],"metadata-based":[176],"indicators":[177],"without":[178],"need":[180],"open":[182],"execute":[184],"files.":[185],"Feature":[186],"selection":[187],"performed":[189],"using":[190,214,258],"combination":[192],"SHAP":[194],"analysis":[195,254],"importance,":[198],"yielding":[199],"compact,":[200],"discriminative":[201],"subsets":[203],"each":[205],"type.":[207],"selected":[209],"features":[210],"were":[211,242],"evaluated":[212],"lightweight":[215,260],"machine":[216],"learning":[217],"including":[219],"Random":[220],"Forest,":[221],"XGBoost,":[222],"Decision":[224],"Tree.":[225],"All":[226],"models":[227],"demonstrate":[228],"high":[229],"detection":[230,245],"accuracy":[231],"code-based":[236],"(quishing),":[238],"two":[239],"complementary":[240],"implemented:":[243],"image-based":[244],"employing":[247],"Convolutional":[248],"Neural":[249],"Networks":[250],"(CNNs)":[251],"lexical":[253],"decoded":[256],"recent":[259],"language":[261],"models.":[262]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-11T00:00:00"}