{"id":"https://openalex.org/W7117529372","doi":"https://doi.org/10.1109/iisa66859.2025.11311242","title":"Exposing ToS-Based Traffic Manipulation with Deep Learning Models","display_name":"Exposing ToS-Based Traffic Manipulation with Deep Learning Models","publication_year":2025,"publication_date":"2025-07-10","ids":{"openalex":"https://openalex.org/W7117529372","doi":"https://doi.org/10.1109/iisa66859.2025.11311242"},"language":null,"primary_location":{"id":"doi:10.1109/iisa66859.2025.11311242","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iisa66859.2025.11311242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information, Intelligence, Systems &amp;amp; Applications (IISA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057164541","display_name":"Christos Christodoulou","orcid":"https://orcid.org/0000-0003-3843-9118"},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Christos Christodoulou","raw_affiliation_strings":["University of the Aegean,Department of Cultural Technology and Communication,Mitilini,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of the Aegean,Department of Cultural Technology and Communication,Mitilini,Greece","institution_ids":["https://openalex.org/I98805295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015433411","display_name":"Emmanouil Mavrikos","orcid":"https://orcid.org/0000-0001-5831-5636"},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Emmanouil Mavrikos","raw_affiliation_strings":["University of the Aegean,Department of Cultural Technology and Communication,Mitilini,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of the Aegean,Department of Cultural Technology and Communication,Mitilini,Greece","institution_ids":["https://openalex.org/I98805295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003619823","display_name":"Christos Kalloniatis","orcid":"https://orcid.org/0000-0002-8844-2596"},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Christos Kalloniatis","raw_affiliation_strings":["University of the Aegean,Department of Cultural Technology and Communication,Mitilini,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of the Aegean,Department of Cultural Technology and Communication,Mitilini,Greece","institution_ids":["https://openalex.org/I98805295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084644811","display_name":"George E. Tsekouras","orcid":"https://orcid.org/0000-0001-7006-1536"},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"George Tsekouras","raw_affiliation_strings":["University of the Aegean,Department of Cultural Technology and Communication,Mitilini,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of the Aegean,Department of Cultural Technology and Communication,Mitilini,Greece","institution_ids":["https://openalex.org/I98805295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.78157628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9613999724388123,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9613999724388123,"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":0.026000000536441803,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.0052999998442828655,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6586999893188477},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5516999959945679},{"id":"https://openalex.org/keywords/deep-packet-inspection","display_name":"Deep packet inspection","score":0.4977000057697296},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.47049999237060547},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.45170000195503235},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42590001225471497},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.4041999876499176},{"id":"https://openalex.org/keywords/traffic-classification","display_name":"Traffic classification","score":0.39899998903274536},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3734000027179718}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7396000027656555},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6586999893188477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6248999834060669},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5516999959945679},{"id":"https://openalex.org/C204679922","wikidata":"https://www.wikidata.org/wiki/Q734252","display_name":"Deep packet inspection","level":3,"score":0.4977000057697296},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4968999922275543},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.45170000195503235},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.39899998903274536},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3292999863624573},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C172173386","wikidata":"https://www.wikidata.org/wiki/Q79984","display_name":"Ethernet","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3037000000476837},{"id":"https://openalex.org/C35341882","wikidata":"https://www.wikidata.org/wiki/Q8795","display_name":"Internet Protocol","level":3,"score":0.28130000829696655},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2678999900817871},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2597000002861023},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25529998540878296},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.25099998712539673},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iisa66859.2025.11311242","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iisa66859.2025.11311242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information, Intelligence, Systems &amp;amp; Applications (IISA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.41988757252693176}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2073089243","https://openalex.org/W2096118443","https://openalex.org/W2133663884","https://openalex.org/W2169694115","https://openalex.org/W2246348251","https://openalex.org/W2343828539","https://openalex.org/W2750674396","https://openalex.org/W2752291283","https://openalex.org/W2753429870","https://openalex.org/W3000773800","https://openalex.org/W3127291124","https://openalex.org/W3191409033","https://openalex.org/W4293056343","https://openalex.org/W4377970583","https://openalex.org/W4392399561","https://openalex.org/W4394910683","https://openalex.org/W4396769364","https://openalex.org/W4403864240"],"related_works":[],"abstract_inverted_index":{"Contemporary":[0],"Ethernet":[1],"networks":[2],"rely":[3],"on":[4,9],"traffic":[5,38,88,117],"classification":[6],"techniques":[7],"based":[8],"packet":[10],"labeling":[11],"fields":[12,112],"such":[13],"as":[14,39,70,122],"the":[15,86,110,202,214],"Type":[16],"of":[17,68,113,184,204],"Service":[18],"(ToS).":[19],"These":[20],"mechanisms,":[21],"while":[22,187],"effective":[23],"in":[24,190,221],"prioritizing":[25],"latency-sensitive":[26],"flows,":[27],"are":[28],"vulnerable":[29],"to":[30,49,118],"manipulation.":[31],"Malicious":[32],"actors":[33],"can":[34],"falsely":[35,119],"label":[36],"low-priority":[37],"highpriority":[40],"(e.g.,":[41],"Voice":[42],"or":[43],"Video),":[44],"thereby":[45],"gaining":[46],"unfair":[47],"access":[48],"bandwidth":[50],"and":[51,74,98,115,124,137,144,156,168,179,212,219],"degrading":[52],"network":[53],"fairness.":[54],"This":[55],"study":[56],"introduces":[57],"a":[58],"machine":[59],"learning":[60,129,206],"approach":[61],"for":[62,208,216],"anomaly":[63,210],"detection,":[64],"leveraging":[65],"behavioral":[66],"attributes":[67],"packets-such":[69],"size,":[71],"interarrival":[72],"time,":[73],"protocol":[75],"port-to":[76],"identify":[77],"suspicious":[78],"misclassifications.":[79],"A":[80],"comprehensive":[81],"dataset":[82],"was":[83],"generated":[84],"using":[85],"D-ITGBox":[87],"creation":[89],"tool,":[90],"emulating":[91],"audio":[92],"(G.721),":[93],"video":[94],"(H.264),":[95],"background":[96,114],"(ICMP),":[97],"best-effort":[99,116],"(HTTP/Telnet)":[100],"flows.":[101],"Additionally,":[102],"two":[103],"anomalous":[104],"datasets":[105],"were":[106,158,170],"synthesized":[107],"by":[108],"modifying":[109],"ToS":[111],"represent":[120],"them":[121],"voice":[123],"video,":[125],"respectively.":[126],"Three":[127],"deep":[128,205],"models-Convolutional":[130],"Neural":[131],"Networks":[132],"(CNN),":[133],"Multi-Layer":[134],"Perceptrons":[135],"(MLP),":[136],"Long":[138],"Short-Term":[139],"Memory":[140],"(LSTM)":[141],"networks-were":[142],"trained":[143],"evaluated":[145],"across":[146],"50":[147],"randomized":[148],"iterations.":[149],"Performance":[150],"metrics":[151],"including":[152],"accuracy,":[153],"precision,":[154],"recall,":[155],"F1-score":[157],"analyzed.":[159],"To":[160],"validate":[161],"performance":[162],"differences,":[163],"non-parametric":[164],"statistical":[165],"tests":[166],"(Friedman":[167],"Wilcoxon)":[169],"applied.":[171],"Results":[172],"revealed":[173],"that":[174],"LSTM":[175],"achieved":[176],"superior":[177],"recall":[178],"F1-score,":[180],"indicating":[181],"robust":[182],"detection":[183,211],"sequential":[185],"anomalies,":[186],"MLP":[188],"excelled":[189],"precision.":[191],"CNN":[192],"demonstrated":[193],"balanced":[194],"but":[195],"more":[196],"variable":[197],"performance.":[198],"The":[199],"findings":[200],"advocate":[201],"application":[203],"models":[207],"Ethernet-based":[209],"set":[213],"groundwork":[215],"hybrid":[217],"approaches":[218],"deployment":[220],"real-time":[222],"environments.":[223]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-30T00:00:00"}
