{"id":"https://openalex.org/W7224178506","doi":"https://doi.org/10.5281/zenodo.23765162","title":"Adversarial_Malware_Detection","display_name":"Adversarial_Malware_Detection","publication_year":2026,"publication_date":"2026-10-29","ids":{"openalex":"https://openalex.org/W7224178506","doi":"https://doi.org/10.5281/zenodo.23765162"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.23765162","doi":"https://doi.org/10.5281/zenodo.23765162","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23765162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.23765162","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160140349","display_name":"Sammi Azaz","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azaz, Sammi","raw_affiliation_strings":["IILM University, Greater Noida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IILM University, Greater Noida","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075180456","display_name":"Raushan Kumar","orcid":"https://orcid.org/0000-0002-3275-7492","observed_orcids":["https://orcid.org/0000-0002-3275-7492"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Raushan","raw_affiliation_strings":["IILM University, Greater Noida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IILM University, Greater Noida","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114148058","display_name":"Priyanshu Ranjan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ranjan, Priyanshu","raw_affiliation_strings":["IILM University, Greater Noida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IILM University, Greater Noida","institution_ids":[]}]}],"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.5249000191688538,"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"}},"topics":[{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.5249000191688538,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.4722000062465668,"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/T11424","display_name":"Security and Verification in Computing","score":0.002300000051036477,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/malware-detection","display_name":"malware detection","score":0.9599999785423279},{"id":"https://openalex.org/keywords/adversarial-robustness","display_name":"adversarial robustness","score":0.8460000157356262},{"id":"https://openalex.org/keywords/binary-image-classification","display_name":"binary image classification","score":0.7300000190734863},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8820000290870667},{"id":"https://openalex.org/keywords/fgsm","display_name":"FGSM","score":0.9160000085830688},{"id":"https://openalex.org/keywords/white-box-attacks","display_name":"white-box attacks","score":0.7049999833106995},{"id":"https://openalex.org/keywords/adversarial-perturbations","display_name":"adversarial perturbations","score":0.8019999861717224},{"id":"https://openalex.org/keywords/zero-day-malware","display_name":"zero-day malware","score":0.5770000219345093},{"id":"https://openalex.org/keywords/obfuscated-malware","display_name":"obfuscated malware","score":0.9049999713897705},{"id":"https://openalex.org/keywords/adversarial-training","display_name":"adversarial training","score":0.6019999980926514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7473999857902527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7044000029563904},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6229000091552734},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5559999942779541},{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.5284000039100647},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5221999883651733},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5110999941825867},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4214000105857849},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.4156000018119812},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4016999900341034},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.40049999952316284},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3767000138759613},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.29649999737739563},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C180932941","wikidata":"https://www.wikidata.org/wiki/Q997233","display_name":"White box","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.25780001282691956}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.23765162","doi":"https://doi.org/10.5281/zenodo.23765162","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23765162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.23765162","doi":"https://doi.org/10.5281/zenodo.23765162","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23765162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2432142698","https://openalex.org/W7109095217","https://openalex.org/W4400020428","https://openalex.org/W2896732478","https://openalex.org/W2744095836","https://openalex.org/W2789987041","https://openalex.org/W4394879187","https://openalex.org/W4392473233","https://openalex.org/W4417003264","https://openalex.org/W4413680185"],"abstract_inverted_index":{"Modern":[0],"cybersecurity":[1],"relies":[2],"increasingly":[3],"on":[4,95,130],"automated":[5],"machine":[6,163],"learning":[7,31,164],"classifiers":[8],"to":[9,20,46,65,137],"safeguard":[10],"systems":[11],"against":[12,85],"malicious":[13],"software.":[14],"Traditional":[15],"signature-based":[16],"detection":[17],"mechanisms":[18,160],"fail":[19],"identify":[21],"zero-day":[22],"and":[23,80,100,157],"obfuscated":[24],"threats,":[25],"prompting":[26],"a":[27,91],"shift":[28],"toward":[29],"deep":[30],"architectures.":[32],"Visualizing":[33],"raw":[34],"binary":[35,76,97],"executables":[36],"as":[37],"two-dimensional":[38],"grayscale":[39],"images":[40],"allows":[41],"Convolutional":[42],"Neural":[43],"Networks":[44],"(CNNs)":[45],"extract":[47],"rich":[48],"spatial":[49],"texture":[50],"patterns":[51],"without":[52],"executing":[53],"untrusted":[54],"code.":[55],"However,":[56],"neural":[57],"network":[58],"models":[59],"exhibit":[60],"critical":[61],"vulnerabilities":[62],"when":[63],"subjected":[64],"targeted":[66],"adversarial":[67,155],"perturbations.":[68],"This":[69],"paper":[70],"presents":[71],"an":[72,123,140],"end-to-end":[73],"framework":[74],"for":[75],"malware":[77],"image":[78,98],"classification":[79,126],"systematically":[81],"evaluates":[82],"model":[83],"robustness":[84],"white-box":[86],"gradient-based":[87],"attacks.":[88],"We":[89],"construct":[90],"CNN":[92,121],"classifier":[93],"trained":[94],"converted":[96],"representations":[99],"integrate":[101],"the":[102,109,119,151],"Fast":[103],"Gradient":[104],"Sign":[105],"Method":[106],"(FGSM)":[107],"using":[108],"Adversarial":[110],"Robustness":[111],"Toolbox":[112],"(ART).":[113],"Empirical":[114],"evaluation":[115,156],"demonstrates":[116],"that":[117],"while":[118],"proposed":[120],"achieves":[122],"optimal":[124],"baseline":[125],"accuracy":[127],"of":[128,144,153],"96.2%":[129],"unperturbed":[131],"data,":[132],"its":[133],"performance":[134],"degrades":[135],"down":[136],"18.7%":[138],"under":[139],"FGSM":[141],"perturbation":[142],"factor":[143],"\u03b5":[145],"=":[146],"0.30.":[147],"These":[148],"findings":[149],"highlight":[150],"necessity":[152],"incorporating":[154],"defensive":[158],"training":[159],"into":[161],"production-level":[162],"security":[165],"infrastructure.":[166]},"counts_by_year":[],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2026-10-30T00:00:00"}