{"id":"https://openalex.org/W7169764606","doi":"https://doi.org/10.48550/arxiv.2607.15379","title":"On the Impact of Entropy-based Features","display_name":"On the Impact of Entropy-based Features","publication_year":2026,"publication_date":"2026-07-16","ids":{"openalex":"https://openalex.org/W7169764606","doi":"https://doi.org/10.48550/arxiv.2607.15379"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.15379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15379","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":null,"license_id":null,"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.2607.15379","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141219855","display_name":"Iuri Mundstock","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mundstock, Iuri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114234346","display_name":"Abreu Quevedo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quevedo, Abreu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119592355","display_name":"Jeferson Campos Nobre","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nobre, J\u00e9ferson Campos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141195293","display_name":"Roben C. Lunardi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lunardi, Roben C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141174787","display_name":"Thiago L. T. da Silveira","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"da Silveira, Thiago L. T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135344333","display_name":"Bruno L. Dalmazo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dalmazo, Bruno L.","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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.7039999961853027,"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":0.7039999961853027,"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":0.23510000109672546,"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/T11644","display_name":"Spam and Phishing Detection","score":0.006500000134110451,"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/interpretability","display_name":"Interpretability","score":0.843999981880188},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5803999900817871},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5799000263214111},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4372999966144562},{"id":"https://openalex.org/keywords/traffic-analysis","display_name":"Traffic analysis","score":0.40630000829696655},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.38920000195503235},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.35929998755455017},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.322299987077713}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.843999981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6421999931335449},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5803999900817871},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5799000263214111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5619999766349792},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.550599992275238},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5489000082015991},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4372999966144562},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.35929998755455017},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.322299987077713},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2996000051498413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.15379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15379","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.15379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15379","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Network":[0],"anomaly":[1,147],"detection":[2,99,148],"is":[3,48,152],"increasingly":[4],"challenging":[5],"due":[6],"to":[7,39,49,52,144,170],"the":[8,31,68,108,132],"growing":[9],"diversity":[10],"and":[11,77,89,141,161],"variability":[12,54],"of":[13,33,116],"traffic":[14,43,57,126,173],"patterns,":[15],"which":[16],"are":[17,163],"not":[18],"always":[19],"well":[20],"captured":[21],"by":[22],"traditional":[23],"statistical":[24],"features.":[25,174],"In":[26],"this":[27,91],"work,":[28],"we":[29],"explore":[30],"use":[32,50],"entropy":[34,51,166],"as":[35],"an":[36],"additional":[37,109],"feature":[38,70,159],"support":[40],"supervised":[41],"network":[42],"classification.":[44],"The":[45,114],"main":[46],"idea":[47],"represent":[53],"in":[55,104,122,125,155],"selected":[56],"attributes,":[58],"complementing":[59],"conventional":[60],"descriptors":[61],"rather":[62],"than":[63],"replacing":[64],"them.":[65],"We":[66],"integrate":[67],"entropy-based":[69,136],"into":[71],"a":[72,82,96,120,139,167],"standard":[73],"machine":[74],"learning":[75],"pipeline":[76],"evaluate":[78],"its":[79],"impact":[80],"through":[81],"direct":[83],"comparison":[84],"between":[85],"models":[86],"trained":[87],"with":[88,128],"without":[90],"feature.":[92],"Experiments":[93],"conducted":[94],"on":[95],"public":[97],"intrusion":[98],"dataset":[100],"show":[101],"consistent":[102],"improvements":[103],"classification":[105],"performance,":[106],"while":[107],"computational":[110],"cost":[111],"remains":[112],"low.":[113],"analysis":[115],"confusion":[117],"matrices":[118],"indicates":[119],"reduction":[121],"misclassifications,":[123],"especially":[124],"scenarios":[127],"higher":[129],"variability.":[130],"Overall,":[131],"results":[133],"suggest":[134],"that":[135],"features":[137],"offer":[138],"simple":[140],"practical":[142],"way":[143],"enhance":[145],"existing":[146],"pipelines.":[149],"This":[150],"approach":[151],"particularly":[153],"attractive":[154],"settings":[156],"where":[157],"lightweight":[158],"engineering":[160],"interpretability":[162],"important,":[164],"making":[165],"useful":[168],"complement":[169],"commonly":[171],"used":[172]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-21T00:00:00"}
