{"id":"https://openalex.org/W7161704613","doi":"https://doi.org/10.48550/arxiv.2605.18666","title":"A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?","display_name":"A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161704613","doi":"https://doi.org/10.48550/arxiv.2605.18666"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18666","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18666","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.2605.18666","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136497298","display_name":"Mohamed elShehaby","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"elShehaby, Mohamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103160400","display_name":"Ashraf Matrawy","orcid":"https://orcid.org/0000-0001-9220-4630"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matrawy, Ashraf","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.96670001745224,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.96670001745224,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.005799999926239252,"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/T10400","display_name":"Network Security and Intrusion Detection","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/adversarial-system","display_name":"Adversarial system","score":0.8851000070571899},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5759999752044678},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5199999809265137},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5121999979019165},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5004000067710876},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.46320000290870667},{"id":"https://openalex.org/keywords/intrusion","display_name":"Intrusion","score":0.4512999951839447},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.4361000061035156}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8851000070571899},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7264999747276306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5770000219345093},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5759999752044678},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5199999809265137},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5121999979019165},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5004000067710876},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.46320000290870667},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.4512999951839447},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.4361000061035156},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.414000004529953},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3662000000476837},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.36329999566078186},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C164614171","wikidata":"https://www.wikidata.org/wiki/Q5204775","display_name":"DECIPHER","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18666","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18666","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.2605.18666","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18666","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":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4547080993652344}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Gradient-based":[0],"adversarial":[1,78],"attacks":[2],"subtly":[3],"manipulate":[4],"inputs":[5],"of":[6,44,109],"Machine":[7],"Learning":[8],"(ML)":[9],"models":[10],"to":[11],"induce":[12],"incorrect":[13],"predictions.":[14],"This":[15],"paper":[16],"investigates":[17],"whether":[18],"careful":[19],"architectural":[20],"choices":[21],"alone":[22],"can":[23],"yield":[24],"an":[25],"inherently":[26],"robust":[27],"Deep":[28],"Neural":[29],"Network":[30,32],"(DNN)-based":[31],"Intrusion":[33],"Detection":[34],"Systems":[35],"(NIDS),":[36],"without":[37],"any":[38],"additional":[39],"explicit":[40],"defenses.":[41],"Through":[42],"thousands":[43],"experiments,":[45],"around":[46],"2200,":[47],"varying":[48],"network":[49],"depth,":[50],"feature":[51,69],"dimensionality,":[52],"activation":[53,73],"functions,":[54],"and":[55,60,71,75,98],"dropout":[56],"across":[57],"FGSM,":[58],"PGD,":[59],"BIM":[61],"attacks,":[62],"we":[63],"show":[64],"that":[65],"shallower":[66],"networks,":[67],"reduced":[68],"sets,":[70],"ReLU":[72],"consistently":[74],"jointly":[76],"reduce":[77],"vulnerability.":[79],"Moreover,":[80],"a":[81],"simple":[82],"model":[83],"following":[84],"this":[85],"recipe":[86],"outperforms":[87],"deeper,":[88],"fully-featured":[89],"adversarially":[90],"trained":[91],"models,":[92],"while":[93,103],"maintaining":[94],"near-perfect":[95],"clean-traffic":[96],"detection":[97],"lower":[99],"training":[100],"times.":[101],"Nevertheless,":[102],"less":[104,112],"is":[105,113],"more,":[106],"the":[107,110],"selection":[108],"right":[111],"what":[114],"truly":[115],"matters.":[116]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}