{"id":"https://openalex.org/W3045747374","doi":"https://doi.org/10.1145/3407197.3407608","title":"Memristor Based Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Intrusion Detection","display_name":"Memristor Based Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Intrusion Detection","publication_year":2020,"publication_date":"2020-07-28","ids":{"openalex":"https://openalex.org/W3045747374","doi":"https://doi.org/10.1145/3407197.3407608","mag":"3045747374"},"language":"en","primary_location":{"id":"doi:10.1145/3407197.3407608","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3407197.3407608","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3407197.3407608","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Neuromorphic Systems 2020","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3407197.3407608","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027794360","display_name":"Md. Shahanur Alam","orcid":"https://orcid.org/0000-0002-2143-3855"},"institutions":[{"id":"https://openalex.org/I127591826","display_name":"University of Dayton","ror":"https://ror.org/021v3qy27","country_code":"US","type":"education","lineage":["https://openalex.org/I127591826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Shahanur Alam","raw_affiliation_strings":["University of Dayton, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton, USA","institution_ids":["https://openalex.org/I127591826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055959085","display_name":"Chris Yakopcic","orcid":"https://orcid.org/0000-0001-6401-272X"},"institutions":[{"id":"https://openalex.org/I127591826","display_name":"University of Dayton","ror":"https://ror.org/021v3qy27","country_code":"US","type":"education","lineage":["https://openalex.org/I127591826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chris Yakopcic","raw_affiliation_strings":["University of Dayton, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton, USA","institution_ids":["https://openalex.org/I127591826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037351559","display_name":"Guru Subramanyam","orcid":"https://orcid.org/0000-0003-2871-0277"},"institutions":[{"id":"https://openalex.org/I127591826","display_name":"University of Dayton","ror":"https://ror.org/021v3qy27","country_code":"US","type":"education","lineage":["https://openalex.org/I127591826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guru Subramanyam","raw_affiliation_strings":["University of Dayton, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton, USA","institution_ids":["https://openalex.org/I127591826"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104090957","display_name":"Tarek M. Taha","orcid":null},"institutions":[{"id":"https://openalex.org/I127591826","display_name":"University of Dayton","ror":"https://ror.org/021v3qy27","country_code":"US","type":"education","lineage":["https://openalex.org/I127591826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tarek M. Taha","raw_affiliation_strings":["University of Dayton, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton, USA","institution_ids":["https://openalex.org/I127591826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I127591826"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9987999796867371,"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/T13182","display_name":"Quantum-Dot Cellular Automata","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/memristor","display_name":"Memristor","score":0.7926505208015442},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.7777734994888306},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7404308319091797},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5500609874725342},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5473324656486511},{"id":"https://openalex.org/keywords/adaptive-resonance-theory","display_name":"Adaptive resonance theory","score":0.4968748390674591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49148571491241455},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.45552951097488403},{"id":"https://openalex.org/keywords/capacitor","display_name":"Capacitor","score":0.44604814052581787},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.43932095170021057},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3272392153739929},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.28346478939056396},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.204548180103302},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14191338419914246},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.11949312686920166}],"concepts":[{"id":"https://openalex.org/C150072547","wikidata":"https://www.wikidata.org/wiki/Q212923","display_name":"Memristor","level":2,"score":0.7926505208015442},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.7777734994888306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404308319091797},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5500609874725342},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5473324656486511},{"id":"https://openalex.org/C115755159","wikidata":"https://www.wikidata.org/wiki/Q352487","display_name":"Adaptive resonance theory","level":3,"score":0.4968748390674591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49148571491241455},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.45552951097488403},{"id":"https://openalex.org/C52192207","wikidata":"https://www.wikidata.org/wiki/Q5322","display_name":"Capacitor","level":3,"score":0.44604814052581787},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.43932095170021057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3272392153739929},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.28346478939056396},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.204548180103302},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14191338419914246},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11949312686920166},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3407197.3407608","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3407197.3407608","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3407197.3407608","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Neuromorphic Systems 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3407197.3407608","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3407197.3407608","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3407197.3407608","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Neuromorphic Systems 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2861184789","display_name":"SHF:Small:Neuromorphic Architectures for On-line Learning","funder_award_id":"1718633","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3045747374.pdf","grobid_xml":"https://content.openalex.org/works/W3045747374.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1973433558","https://openalex.org/W1982304603","https://openalex.org/W1997629593","https://openalex.org/W2087261283","https://openalex.org/W2131498344","https://openalex.org/W2144219012","https://openalex.org/W2162651880","https://openalex.org/W2182574237","https://openalex.org/W2248832573","https://openalex.org/W2556583623","https://openalex.org/W2620661538","https://openalex.org/W2734779510","https://openalex.org/W2747715470","https://openalex.org/W2804391406","https://openalex.org/W2897568615","https://openalex.org/W2897792541","https://openalex.org/W2902106343","https://openalex.org/W2903651781","https://openalex.org/W2947372880","https://openalex.org/W2956917438","https://openalex.org/W2963311299","https://openalex.org/W2963739929","https://openalex.org/W2965043358","https://openalex.org/W2968985274","https://openalex.org/W2971215800","https://openalex.org/W2972234767","https://openalex.org/W2973153123","https://openalex.org/W2974425144","https://openalex.org/W2999491543"],"related_works":["https://openalex.org/W1872623660","https://openalex.org/W3207218810","https://openalex.org/W4292697011","https://openalex.org/W3212508523","https://openalex.org/W1995352804","https://openalex.org/W2909534142","https://openalex.org/W2086672837","https://openalex.org/W4367187682","https://openalex.org/W1940420793","https://openalex.org/W3215957123"],"abstract_inverted_index":{"As":[0],"computer":[1],"networks":[2],"become":[3],"more":[4],"advanced,":[5],"the":[6,77,82,87,103,109,131,135,152,159],"necessity":[7],"for":[8,33,85,130],"reliable":[9],"intrusion":[10,35],"detection":[11,121,164],"at":[12],"extreme":[13],"efficiency":[14],"has":[15],"vastly":[16],"increased.":[17],"Thus,":[18],"in":[19,42,102],"this":[20],"work":[21],"we":[22],"present":[23],"a":[24,69,95],"one":[25],"shot":[26],"learning":[27,32,144,162],"system":[28,50,110,136],"capable":[29,52],"of":[30,53,108,148],"online":[31],"network":[34],"detection.":[36],"Adaptive":[37],"resonance":[38],"theory":[39],"is":[40,51,66,126],"implemented":[41,67],"custom":[43],"low":[44],"power":[45],"memristor-based":[46],"neuromorphic":[47],"hardware.":[48],"The":[49,61,90,106,120,146],"discriminating":[54],"with":[55,68],"existing":[56],"knowledge":[57],"to":[58,93,100,157],"learn":[59],"incrementally.":[60],"winner":[62],"take":[63],"all":[64],"circuitry":[65],"capacitor":[70,153],"and":[71,80,117,128,143,163],"CMOS":[72],"timing":[73],"circuit":[74],"that":[75],"finds":[76],"winning":[78,88,96],"neuron":[79,97],"controls":[81],"weight":[83],"update":[84],"only":[86],"neuron.":[89],"time":[91],"required":[92],"find":[94],"was":[98,111,137,154],"determined":[99],"be":[101],"nanosecond":[104],"range.":[105],"performance":[107],"evaluated":[112],"on":[113,161],"both":[114],"previously":[115],"known":[116,132],"zero-day":[118,124],"datasets.":[119],"accuracy":[122],"using":[123,139],"packets":[125],"99.97%,":[127],"99.99%":[129],"attacks.":[133],"Furthermore,":[134],"tested":[138],"various":[140],"vigilance":[141],"parameters":[142],"rates.":[145],"variation":[147],"threshold":[149],"voltage":[150],"across":[151],"also":[155],"investigated":[156],"observe":[158],"effect":[160],"accuracy.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}