{"id":"https://openalex.org/W7125954607","doi":"https://doi.org/10.1111/exsy.70217","title":"Securing the Unseen: A Comprehensive Exploration Review of AI \u2010Powered Models for Zero\u2010Day Attack Detection","display_name":"Securing the Unseen: A Comprehensive Exploration Review of AI \u2010Powered Models for Zero\u2010Day Attack Detection","publication_year":2026,"publication_date":"2026-01-28","ids":{"openalex":"https://openalex.org/W7125954607","doi":"https://doi.org/10.1111/exsy.70217"},"language":"en","primary_location":{"id":"doi:10.1111/exsy.70217","is_oa":true,"landing_page_url":"https://doi.org/10.1111/exsy.70217","pdf_url":null,"source":{"id":"https://openalex.org/S72232612","display_name":"Expert Systems","issn_l":"0266-4720","issn":["0266-4720","1468-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1111/exsy.70217","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110915386","display_name":"Abdullah Al Siam","orcid":null},"institutions":[{"id":"https://openalex.org/I200606013","display_name":"Daffodil International University","ror":"https://ror.org/052t4a858","country_code":"BD","type":"education","lineage":["https://openalex.org/I200606013"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Abdullah Al Siam","raw_affiliation_strings":["Department of Software Engineering Daffodil International University  Dhaka Bangladesh"],"raw_orcid":"https://orcid.org/0009-0006-6861-6188","affiliations":[{"raw_affiliation_string":"Department of Software Engineering Daffodil International University  Dhaka Bangladesh","institution_ids":["https://openalex.org/I200606013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012720037","display_name":"Nuruzzaman Faruqui","orcid":"https://orcid.org/0000-0001-9306-9637"},"institutions":[{"id":"https://openalex.org/I200606013","display_name":"Daffodil International University","ror":"https://ror.org/052t4a858","country_code":"BD","type":"education","lineage":["https://openalex.org/I200606013"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Nuruzzaman Faruqui","raw_affiliation_strings":["Department of Software Engineering Daffodil International University  Dhaka Bangladesh"],"raw_orcid":"https://orcid.org/0000-0001-9306-9637","affiliations":[{"raw_affiliation_string":"Department of Software Engineering Daffodil International University  Dhaka Bangladesh","institution_ids":["https://openalex.org/I200606013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122728248","display_name":"A K M Azad","orcid":"https://orcid.org/0000-0002-5251-2214"},"institutions":[{"id":"https://openalex.org/I240666556","display_name":"Imam Mohammad ibn Saud Islamic University","ror":"https://ror.org/05gxjyb39","country_code":"SA","type":"education","lineage":["https://openalex.org/I240666556"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Akm Azad","raw_affiliation_strings":["Department of Mathematics and Statistics Imam Mohammad Ibn Saud Islamic University (IMSIU)  Riyadh Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0002-5251-2214","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics Imam Mohammad Ibn Saud Islamic University (IMSIU)  Riyadh Saudi Arabia","institution_ids":["https://openalex.org/I240666556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034828056","display_name":"Mohammad Ali Moni","orcid":"https://orcid.org/0000-0003-0756-1006"},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]},{"id":"https://openalex.org/I240666556","display_name":"Imam Mohammad ibn Saud Islamic University","ror":"https://ror.org/05gxjyb39","country_code":"SA","type":"education","lineage":["https://openalex.org/I240666556"]}],"countries":["AU","SA"],"is_corresponding":true,"raw_author_name":"Mohammad Ali Moni","raw_affiliation_strings":["AI and Digital Health Technology, AI and Cyber Futures Institute Charles Sturt University  Bathurst New South Wales Australia","AI and Digital Health Technology, Rural Health Research Institute Charles Sturt University  Orange New South Wales Australia","Health Sciences Research Center (HSRC), Deanship of Scientific Research Imam Mohammad Ibn Saud Islamic University (IMSIU)  Riyadh Saudi\u00a0Arabia"],"raw_orcid":"https://orcid.org/0000-0003-0756-1006","affiliations":[{"raw_affiliation_string":"AI and Digital Health Technology, AI and Cyber Futures Institute Charles Sturt University  Bathurst New South Wales Australia","institution_ids":["https://openalex.org/I153230381"]},{"raw_affiliation_string":"AI and Digital Health Technology, Rural Health Research Institute Charles Sturt University  Orange New South Wales Australia","institution_ids":["https://openalex.org/I153230381"]},{"raw_affiliation_string":"Health Sciences Research Center (HSRC), Deanship of Scientific Research Imam Mohammad Ibn Saud Islamic University (IMSIU)  Riyadh Saudi\u00a0Arabia","institution_ids":["https://openalex.org/I240666556"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034828056"],"corresponding_institution_ids":["https://openalex.org/I153230381","https://openalex.org/I240666556"],"apc_list":{"value":3600,"currency":"USD","value_usd":3600},"apc_paid":{"value":3600,"currency":"USD","value_usd":3600},"fwci":21.2094,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.98871713,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"43","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.6449999809265137,"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"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.6449999809265137,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.08139999955892563,"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/T12479","display_name":"Web Application Security Vulnerabilities","score":0.037700001150369644,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.5389999747276306},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5166000127792358},{"id":"https://openalex.org/keywords/comparability","display_name":"Comparability","score":0.5130000114440918},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48829999566078186},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.48809999227523804},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4715000092983246},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.4489000141620636},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.40709999203681946},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4000000059604645},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.39629998803138733}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9002000093460083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5694000124931335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.558899998664856},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5166000127792358},{"id":"https://openalex.org/C197947376","wikidata":"https://www.wikidata.org/wiki/Q5155608","display_name":"Comparability","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48829999566078186},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4715000092983246},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.4489000141620636},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.39629998803138733},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3407000005245209},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.335999995470047},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3061000108718872},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.2888999879360199},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1111/exsy.70217","is_oa":true,"landing_page_url":"https://doi.org/10.1111/exsy.70217","pdf_url":null,"source":{"id":"https://openalex.org/S72232612","display_name":"Expert Systems","issn_l":"0266-4720","issn":["0266-4720","1468-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:publications/a627a127-d2b4-4622-9d1c-536f9c87dda2","is_oa":true,"landing_page_url":"https://researchoutput.csu.edu.au/en/publications/a627a127-d2b4-4622-9d1c-536f9c87dda2","pdf_url":null,"source":{"id":"https://openalex.org/S7407055442","display_name":"Charles Sturt University Research Output (CRO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Al Siam, A, Faruqui, N, Azad, A & Moni, M A 2026, 'Securing the unseen : A comprehensive exploration review of AI-powered models for zero-day attack detection', Expert Systems, vol. 43, no. 3, e70217, pp. 1-22. https://doi.org/10.1111/exsy.70217","raw_type":"info:eu-repo/semantics/review"}],"best_oa_location":{"id":"doi:10.1111/exsy.70217","is_oa":true,"landing_page_url":"https://doi.org/10.1111/exsy.70217","pdf_url":null,"source":{"id":"https://openalex.org/S72232612","display_name":"Expert Systems","issn_l":"0266-4720","issn":["0266-4720","1468-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1973836200","https://openalex.org/W2101222264","https://openalex.org/W2134269391","https://openalex.org/W2180612164","https://openalex.org/W2414564754","https://openalex.org/W2549585799","https://openalex.org/W2565516711","https://openalex.org/W2615699342","https://openalex.org/W2794460302","https://openalex.org/W2794988934","https://openalex.org/W2809254203","https://openalex.org/W2886020981","https://openalex.org/W2948455542","https://openalex.org/W2952298682","https://openalex.org/W2971144011","https://openalex.org/W2980576170","https://openalex.org/W2982676630","https://openalex.org/W3008071995","https://openalex.org/W3023289002","https://openalex.org/W3025093231","https://openalex.org/W3047132966","https://openalex.org/W3094236223","https://openalex.org/W3107089345","https://openalex.org/W3137260098","https://openalex.org/W3140854437","https://openalex.org/W3160220648","https://openalex.org/W3172875390","https://openalex.org/W3177669185","https://openalex.org/W3196546979","https://openalex.org/W3200780444","https://openalex.org/W3205483739","https://openalex.org/W3206824631","https://openalex.org/W3216519235","https://openalex.org/W4200336673","https://openalex.org/W4206696802","https://openalex.org/W4283786485","https://openalex.org/W4284965199","https://openalex.org/W4286485305","https://openalex.org/W4295308257","https://openalex.org/W4295346826","https://openalex.org/W4295854586","https://openalex.org/W4307725013","https://openalex.org/W4309939034","https://openalex.org/W4310206563","https://openalex.org/W4311166084","https://openalex.org/W4313216189","https://openalex.org/W4315498031","https://openalex.org/W4322493038","https://openalex.org/W4360978672","https://openalex.org/W4383646076","https://openalex.org/W4384916913","https://openalex.org/W4389477908","https://openalex.org/W4390872228","https://openalex.org/W4391454446","https://openalex.org/W4393175755","https://openalex.org/W4393940451","https://openalex.org/W4396855777","https://openalex.org/W4396982201","https://openalex.org/W4398183551","https://openalex.org/W4401732041","https://openalex.org/W4406258990","https://openalex.org/W4408324826","https://openalex.org/W4408893792","https://openalex.org/W4412176299","https://openalex.org/W4412694794","https://openalex.org/W4412803236"],"related_works":[],"abstract_inverted_index":{"ABSTRACT":[0],"Zero\u2010day":[1],"exploits":[2],"remain":[3],"challenging":[4],"to":[5,57,77,203,253],"detect":[6],"because":[7],"they":[8],"often":[9],"appear":[10],"in":[11,67,97],"unknown":[12,259],"distributions":[13],"of":[14,27,100,105,111,193,212,222,229],"signatures":[15],"and":[16,24,48,64,72,80,103,125,170,182,196,206,214,248],"rules.":[17],"The":[18,237],"article":[19],"entails":[20],"a":[21,91,109,198],"systematic":[22],"review":[23,238],"cross\u2010sectional":[25],"synthesis":[26],"four":[28],"fundamental":[29],"model":[30],"families":[31],"for":[32],"identifying":[33],"zero\u2010day":[34,68],"intrusions,":[35],"namely,":[36],"convolutional":[37],"neural":[38,42],"networks":[39,43,46],"(CNN),":[40],"deep":[41],"(DNN),":[44],"Bayesian":[45],"(BN),":[47],"reinforcement":[49],"learning":[50],"(RL).":[51],"A":[52],"PRISMA\u2010style":[53],"protocol":[54,235],"is":[55],"used":[56],"extract":[58],"evidence,":[59],"test":[60,65],"across":[61],"popular":[62],"corpora,":[63],"models":[66,211],"faithful":[69],"regimes,":[70],"time\u2010split,":[71],"cross\u2010dataset":[73],"transfer.":[74],"In":[75],"addition":[76],"aggregate":[78,136],"accuracy":[79,144,166],"F1,":[81],"we":[82],"also":[83],"highlight":[84],"operating\u2010point":[85],"reporting":[86],"the":[87,98,135,155,191,251],"true\u2010positive":[88],"rate":[89],"at":[90,154],"fixed":[92],"false\u2010positive":[93],"rate,":[94],"ranking":[95],"measures":[96,120],"presence":[99],"class":[101],"imbalance,":[102],"calibration":[104],"probability":[106],"predictions":[107],"as":[108,122,174,227],"measure":[110],"expected":[112],"error":[113],"probabilistic":[114],"calibration,":[115,247],"which":[116],"may":[117],"include":[118],"syntactic":[119],"such":[121],"time\u2010to\u2010alert,":[123],"throughput,":[124],"memory":[126],"compute":[127],"footprint.":[128],"Reported":[129],"results":[130],"suggest":[131,197],"that":[132],"DNNs":[133],"demonstrate":[134],"performance":[137],"on":[138,145,148,158,168,187,242],"richly":[139],"feature":[140],"inputs":[141],"(nearly":[142],"99.56%":[143],"CICDDoS2019),":[146],"CNNs":[147],"tensorized":[149],"flows/bytes":[150],"with":[151,164,219],"advantageous":[152],"latency":[153,249],"edge":[156,223],"92.17%":[157],"Bot\u2010IOT),":[159],"BN":[160],"provides":[161,239],"interpretable":[162],"uncertainty":[163],"acceptable":[165],"(99.74%":[167],"NSL\u2010KDD),":[169],"RL":[171],"shows":[172],"promise":[173],"an":[175],"adaptive":[176,256],"detection\u2010response":[177],"when":[178],"there":[179],"are":[180],"rewards":[181],"safe":[183],"training":[184],"environments":[185],"(96.18%":[186],"CSE\u2010CIC\u2010IDS2018).":[188],"We":[189],"unify":[190],"heterogeneity":[192],"our":[194],"datasets":[195],"coherent,":[199],"leakage\u2010wary":[200],"evaluation":[201],"environment":[202],"facilitate":[204],"comparability":[205],"reproducibility.":[207],"Language":[208],"or":[209],"code":[210],"logs":[213],"transformer":[215],"traffic":[216],"encoders,":[217],"along":[218],"lightweight":[220],"backbones":[221],"IDS,":[224],"become":[225],"available":[226],"subjects":[228],"future":[230],"head\u2010to\u2010head":[231],"studies":[232],"under":[233],"equal":[234],"conditions.":[236],"tactical":[240],"advice":[241],"model\u2010data":[243],"fit,":[244],"operating":[245],"points,":[246],"budgets,":[250],"precursor":[252],"deployment":[254],"ready,":[255],"defence":[257],"against":[258],"attacks.":[260]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-01-29T00:00:00"}
