{"id":"https://openalex.org/W3125555237","doi":"https://doi.org/10.1109/igsc51522.2020.9291053","title":"Memristor Based Neuromorphic Network Security System Capable of Online Incremental Learning and Anomaly Detection","display_name":"Memristor Based Neuromorphic Network Security System Capable of Online Incremental Learning and Anomaly Detection","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3125555237","doi":"https://doi.org/10.1109/igsc51522.2020.9291053","mag":"3125555237"},"language":"en","primary_location":{"id":"doi:10.1109/igsc51522.2020.9291053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igsc51522.2020.9291053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 11th International Green and Sustainable Computing Workshops (IGSC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"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,Dept. Of Electrical and Computer Engineering,Dayton,OH,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton,Dept. Of Electrical and Computer Engineering,Dayton,OH,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,Dept. Of Electrical and Computer Engineering,Dayton,OH,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton,Dept. Of Electrical and Computer Engineering,Dayton,OH,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,Dept. Of Electrical and Computer Engineering,Dayton,OH,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton,Dept. Of Electrical and Computer Engineering,Dayton,OH,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,Dept. Of Electrical and Computer Engineering,Dayton,OH,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton,Dept. Of Electrical and Computer Engineering,Dayton,OH,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":false,"cited_by_count":7,"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":0.9994000196456909,"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":0.9994000196456909,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9952999949455261,"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/T12676","display_name":"Machine Learning and ELM","score":0.9850999712944031,"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/autoencoder","display_name":"Autoencoder","score":0.8918511867523193},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.8335106372833252},{"id":"https://openalex.org/keywords/memristor","display_name":"Memristor","score":0.80513596534729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7788643836975098},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7116082310676575},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6746590733528137},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.566529393196106},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.540951132774353},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5016515254974365},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4615362584590912},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42874205112457275},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4264725148677826},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36668407917022705},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3278025686740875},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.32360175251960754},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13912612199783325},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1136019229888916}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8918511867523193},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.8335106372833252},{"id":"https://openalex.org/C150072547","wikidata":"https://www.wikidata.org/wiki/Q212923","display_name":"Memristor","level":2,"score":0.80513596534729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7788643836975098},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7116082310676575},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6746590733528137},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.566529393196106},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.540951132774353},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5016515254974365},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4615362584590912},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42874205112457275},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4264725148677826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36668407917022705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3278025686740875},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32360175251960754},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13912612199783325},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1136019229888916},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igsc51522.2020.9291053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igsc51522.2020.9291053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 11th International Green and Sustainable Computing Workshops (IGSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1973433558","https://openalex.org/W2162651880","https://openalex.org/W2294937515","https://openalex.org/W2331928179","https://openalex.org/W2556583623","https://openalex.org/W2620661538","https://openalex.org/W2623293810","https://openalex.org/W2734779510","https://openalex.org/W2735512542","https://openalex.org/W2747715470","https://openalex.org/W2767677737","https://openalex.org/W2788388592","https://openalex.org/W2790100928","https://openalex.org/W2803255133","https://openalex.org/W2807561401","https://openalex.org/W2886020981","https://openalex.org/W2887670553","https://openalex.org/W2897792541","https://openalex.org/W2902382446","https://openalex.org/W2926701059","https://openalex.org/W2963739929","https://openalex.org/W2970751258","https://openalex.org/W2973153123","https://openalex.org/W2973218493","https://openalex.org/W3014135912","https://openalex.org/W4249837478","https://openalex.org/W4301183454","https://openalex.org/W6749806285"],"related_works":["https://openalex.org/W1872623660","https://openalex.org/W4292697011","https://openalex.org/W3207218810","https://openalex.org/W3212508523","https://openalex.org/W1995352804","https://openalex.org/W2086672837","https://openalex.org/W2909534142","https://openalex.org/W4367187682","https://openalex.org/W3215957123","https://openalex.org/W1940420793"],"abstract_inverted_index":{"Real-time":[0],"network":[1,99],"intrusion":[2,45,54,133],"and":[3,23,34,46,71,103,105,114,161],"anomaly":[4,47,72,81],"detection":[5,48,55,73,82,134],"systems":[6,28],"designed":[7,77],"for":[8,29,97,159],"battery":[9],"powered":[10],"devices":[11],"are":[12,39],"in":[13],"high":[14],"demand.":[15],"This":[16],"paper":[17],"presents":[18],"a":[19],"study":[20],"of":[21,84],"unsupervised":[22],"supervised":[24],"memristor":[25,43,144],"based":[26,44,145],"neuromorphic":[27],"such":[30],"tasks.":[31],"AutoEncoder":[32],"(AE)":[33],"Multilayer":[35],"Perceptron":[36],"(MLP)":[37],"algorithms":[38],"used":[40],"to":[41,124],"design":[42],"systems.":[49],"The":[50,87,131,143],"autoencoder":[51],"shows":[52,147],"strong":[53],"performance":[56],"with":[57,111,137],"accuracy":[58,139],"greater":[59,140],"than":[60,141],"92.5%":[61],"on":[62],"zeroday":[63],"attack":[64,127],"packets.":[65],"A":[66],"real-time":[67,132],"online":[68],"incremental":[69],"learning":[70,88],"system":[74,89,121,135,151],"is":[75,95,109,122],"also":[76],"using":[78,155],"the":[79,85,106,149],"effective":[80],"abilities":[83],"AE.":[86],"uses":[90],"two":[91],"autoencoders,":[92],"one":[93],"AE":[94,108],"pretrained":[96],"classifying":[98],"packets":[100],"as":[101],"normal":[102],"malicious,":[104],"second":[107],"initialized":[110],"random":[112],"weights":[113],"learns":[115],"malicious":[116],"data":[117],"incrementally.":[118],"Thus,":[119],"this":[120],"able":[123],"flag":[125],"new":[126],"classes":[128],"during":[129],"runtime.":[130],"performs":[136],"an":[138],"89.7%.":[142],"implementation":[146],"that":[148],"proposed":[150],"can":[152],"be":[153],"implemented":[154],"extreme":[156],"low":[157],"power":[158],"edge":[160],"IoT":[162],"applications.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
