{"id":"https://openalex.org/W7166778880","doi":"https://doi.org/10.7148/2026-0311","title":"Spam and phishing detection with adversarial attack robustness analysis using machine learning models","display_name":"Spam and phishing detection with adversarial attack robustness analysis using machine learning models","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7166778880","doi":"https://doi.org/10.7148/2026-0311"},"language":null,"primary_location":{"id":"doi:10.7148/2026-0311","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0311","pdf_url":"https://doi.org/10.7148/2026-0311","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.7148/2026-0311","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055653957","display_name":"Anna Plichta","orcid":"https://orcid.org/0000-0001-6503-308X"},"institutions":[{"id":"https://openalex.org/I4210092770","display_name":"Cracow University of Technology","ror":null,"country_code":"PL","type":null,"lineage":["https://openalex.org/I4210092770"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Anna Plichta","raw_affiliation_strings":["Department of Computer Science Cracow University of Technology Cracow , Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Cracow University of Technology Cracow , Poland","institution_ids":["https://openalex.org/I4210092770"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5099380212","display_name":"Gabriela Raczka","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092770","display_name":"Cracow University of Technology","ror":null,"country_code":"PL","type":null,"lineage":["https://openalex.org/I4210092770"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Gabriela Raczka","raw_affiliation_strings":["Department of Computer Science Cracow University of Technology Cracow , Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Cracow University of Technology Cracow , Poland","institution_ids":["https://openalex.org/I4210092770"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210092770"],"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":"311","last_page":"320"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9932000041007996,"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":0.9932000041007996,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.0012000000569969416,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8306000232696533},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.715399980545044},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.48820000886917114},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.45989999175071716},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4318000078201294},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.426800012588501},{"id":"https://openalex.org/keywords/phishing","display_name":"Phishing","score":0.4101000130176544},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.382999986410141}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8306000232696533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7206000089645386},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7196999788284302},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.715399980545044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6815000176429749},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.48820000886917114},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.426800012588501},{"id":"https://openalex.org/C83860907","wikidata":"https://www.wikidata.org/wiki/Q135005","display_name":"Phishing","level":3,"score":0.4101000130176544},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.35899999737739563},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C173483453","wikidata":"https://www.wikidata.org/wiki/Q1040689","display_name":"Synonym (taxonomy)","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C143095724","wikidata":"https://www.wikidata.org/wiki/Q515895","display_name":"Odds","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27900001406669617},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.7148/2026-0311","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0311","pdf_url":"https://doi.org/10.7148/2026-0311","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.7148/2026-0311","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0311","pdf_url":"https://doi.org/10.7148/2026-0311","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166778880.pdf","grobid_xml":"https://content.openalex.org/works/W7166778880.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,244],"article":[1],"presents":[2],"a":[3,33,42,190],"systematic":[4],"investigation":[5],"of":[6,45,249],"machine":[7],"learning":[8],"models":[9,133,229],"for":[10,52,225],"spam":[11,61],"and":[12,19,32,57,105,111,123,160,198,241,253],"phishing":[13],"email":[14,258],"detection,":[15],"emphasising":[16],"adversarial":[17,67,242,251],"robustness":[18],"interpretability.":[20],"Five":[21],"classifiers\u2014Naive":[22],"Bayes,":[23],"Support":[24],"Vector":[25],"Machine,":[26],"Logistic":[27,231],"Regression,":[28],"Dense":[29],"Neural":[30],"Network,":[31],"zero-shot":[34,151],"Large":[35],"Language":[36],"Model":[37],"(ChatGPT":[38],"GPT-4o-mini)\u2014are":[39],"evaluated":[40],"on":[41,140,158,166,184,196],"unified":[43],"corpus":[44],"176,367":[46],"messages":[47],"(Enron-Spam,":[48],"SpamAssassin,":[49],"Phishing":[50],"Email)":[51],"binary":[53,159,202],"(ham":[54,59],"vs.":[55,60,63],"spam)":[56],"multi-class":[58,167,211,219],"generic":[62],"phishing)":[64],"classification.":[65],"Six":[66],"attack":[68,89,185],"strategies":[69],"are":[70,117],"implemented:":[71],"synonym":[72,92,197],"replacement":[73,93],"(WordNet,":[74],"45%":[75],"rate,":[76],"max":[77],"50":[78],"substitutions/document),":[79],"goodword":[80,199],"injection":[81],"(12":[82],"hamindicative":[83],"words,":[84],"5":[85],"phrases),":[86],"explanation-":[87],"guided":[88],"(novel":[90],"SHAP-driven":[91],"targeting":[94],"top-25":[95],"influential":[96],"tokens),":[97],"character-level":[98],"perturbations":[99],"(homoglyphs,":[100],"typos,":[101],"leetspeak),":[102],"rule-based":[103],"paraphrase,":[104],"LLM-driven":[106],"rewriting":[107],"(GPT-4o-mini).":[108],"Adversarial":[109,213],"training":[110,214],"post-hoc":[112],"interpretability":[113],"methods":[114],"(SHAP,":[115],"LIME)":[116],"applied":[118],"to":[119,179],"examine":[120],"defensive":[121],"effectiveness":[122],"model":[124],"transparency.":[125],"Experimental":[126],"findings":[127],"reveal":[128],"an":[129],"accuracy-robustness":[130],"gap:":[131],"trained":[132],"achieve":[134],"robust":[135],"baseline":[136,238],"performance":[137],"(accuracy":[138],"&gt;95%":[139],"clean":[141],"data),":[142],"yet":[143,206],"all":[144],"exhibit":[145],"measurable":[146],"vulnerability":[147],"under":[148],"attack.":[149],"The":[150,187],"LLM":[152,188],"reaches":[153],"89.2%":[154],"accuracy":[155,162],"(F1":[156],"0.896)":[157],"51%":[161],"(macro":[163],"F1":[164,172,194,221],"0.375)":[165],"without":[168],"corpus-specific":[169],"training.":[170],"Binary":[171],"drops":[173,208],"range":[174],"from":[175],"0.39%":[176],"(Logistic":[177],"Regression)":[178,232],"7.25%":[180],"(Dense":[181],"NN)":[182],"depending":[183],"strategy.":[186],"exhibits":[189],"striking":[191],"paradox:":[192],"small":[193],"gains":[195,222],"attacks":[200],"in":[201,210,256],"settings":[203],"(semantic":[204],"robustness),":[205],"substantial":[207],"(\u223c25\u201328%)":[209],"scenarios.":[212],"consistently":[215],"enhances":[216],"robustness,":[217],"yielding":[218],"macro":[220],"exceeding":[223],"69%":[224],"SVM.":[226],"Linear":[227],"TF-IDF":[228],"(SVM,":[230],"emerge":[233],"as":[234],"optimal":[235],"architectures,":[236],"balancing":[237],"accuracy,":[239],"interpretability,":[240],"resilience.":[243],"work":[245],"underscores":[246],"the":[247],"necessity":[248],"explicit":[250],"evaluation":[252],"continuous":[254],"monitoring":[255],"security-critical":[257],"filtering":[259],"systems.":[260]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-07-01T00:00:00"}
