{"id":"https://openalex.org/W3157037691","doi":"https://doi.org/10.1109/icpr48806.2021.9412580","title":"Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection","display_name":"Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3157037691","doi":"https://doi.org/10.1109/icpr48806.2021.9412580","mag":"3157037691"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412580","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"article","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/A5102694051","display_name":"Shibo Zhou","orcid":"https://orcid.org/0009-0006-9292-8093"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shibo Zhou","raw_affiliation_strings":["State University of New York at Binghamton, Binghamton, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State University of New York at Binghamton, Binghamton, NY","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101482100","display_name":"Xiaohua Li","orcid":"https://orcid.org/0000-0001-5837-2470"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohua Li","raw_affiliation_strings":["State University of New York at Binghamton, Binghamton, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State University of New York at Binghamton, Binghamton, NY","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102694051"],"corresponding_institution_ids":["https://openalex.org/I123946342"],"apc_list":null,"apc_paid":null,"fwci":1.1186,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.76998798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"8148","last_page":"8155"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998999834060669,"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":0.9998999834060669,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.914591908454895},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8420020341873169},{"id":"https://openalex.org/keywords/spike","display_name":"Spike (software development)","score":0.7488887310028076},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6013062000274658},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5843316316604614},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.490130752325058},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47155117988586426},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4636356234550476},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4444284439086914},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4330100119113922},{"id":"https://openalex.org/keywords/intrusion","display_name":"Intrusion","score":0.4168005883693695}],"concepts":[{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.914591908454895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8420020341873169},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.7488887310028076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6013062000274658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5843316316604614},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.490130752325058},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47155117988586426},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4636356234550476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4444284439086914},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4330100119113922},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.4168005883693695},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C17409809","wikidata":"https://www.wikidata.org/wiki/Q161764","display_name":"Geochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412580","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1570411240","https://openalex.org/W1977366836","https://openalex.org/W2011778831","https://openalex.org/W2028070713","https://openalex.org/W2099940443","https://openalex.org/W2107433900","https://openalex.org/W2128367023","https://openalex.org/W2168573734","https://openalex.org/W2233731247","https://openalex.org/W2342408547","https://openalex.org/W2399941526","https://openalex.org/W2513853720","https://openalex.org/W2557328120","https://openalex.org/W2569813014","https://openalex.org/W2621826044","https://openalex.org/W2762776925","https://openalex.org/W2775079417","https://openalex.org/W2779025322","https://openalex.org/W2783741806","https://openalex.org/W2798878556","https://openalex.org/W2801844931","https://openalex.org/W2892077605","https://openalex.org/W2904620099","https://openalex.org/W2914117107","https://openalex.org/W2939745396","https://openalex.org/W2963335874","https://openalex.org/W2963760575","https://openalex.org/W2973862992","https://openalex.org/W2984844508","https://openalex.org/W2997180247","https://openalex.org/W3016607949"],"related_works":["https://openalex.org/W2769135912","https://openalex.org/W2174331923","https://openalex.org/W2134068246","https://openalex.org/W2542565870","https://openalex.org/W4306175885","https://openalex.org/W4312604567","https://openalex.org/W4297619707","https://openalex.org/W2583316550","https://openalex.org/W3110622310","https://openalex.org/W2133389611"],"abstract_inverted_index":{"Spiking":[0],"neural":[1,24],"network":[2,132],"(SNN)":[3],"is":[4,18,67,80,122],"interesting":[5],"due":[6],"to":[7,175],"its":[8,16],"strong":[9],"bio-plausibility":[10],"and":[11,47,62,75,107,116,139,158,171],"high":[12],"energy":[13],"efficiency.":[14],"However,":[15],"performance":[17],"falling":[19],"far":[20],"behind":[21],"conventional":[22],"deep":[23],"networks":[25],"(DNNs).":[26],"In":[27],"this":[28,93],"paper,":[29],"considering":[30],"a":[31,105,152],"general":[32],"class":[33],"of":[34,44,88,155],"single-spike":[35],"temporal-coded":[36],"integrate-and-fire":[37],"neurons,":[38],"we":[39,95],"analyze":[40],"the":[41,60,68,84,127,137,140,148],"input-output":[42,64,109],"expressions":[43],"both":[45],"leaky":[46,56],"nonleaky":[48,101],"neurons.":[49],"We":[50],"show":[51,96,146],"that":[52,97,147,166],"SNNs":[53,98,128,150,167],"built":[54,99],"with":[55,100,126],"neurons":[57,102],"suffer":[58],"from":[59],"overly-nonlinear":[61],"overly-complex":[63],"response,":[65],"which":[66,121],"major":[69],"reason":[70,79],"for":[71],"their":[72],"difficult":[73],"training":[74],"low":[76],"performance.":[77],"This":[78,163],"more":[81],"fundamental":[82],"than":[83],"commonly":[85],"believed":[86],"problem":[87],"nondifferentiable":[89],"spikes.":[90],"To":[91],"support":[92],"claim,":[94],"can":[103,112,117,168],"have":[104,118],"less-complex":[106],"less-nonlinear":[108],"response.":[110],"They":[111],"be":[113,169],"easily":[114],"trained":[115],"superior":[119],"performance,":[120],"demonstrated":[123],"by":[124],"experimenting":[125],"over":[129],"two":[130],"popular":[131],"intrusion":[133],"detection":[134],"datasets,":[135],"i.e.,":[136],"NSL-KDD":[138],"AWID":[141],"datasets.":[142],"Our":[143],"experiment":[144],"results":[145],"proposed":[149],"outperform":[151],"comprehensive":[153],"list":[154],"DNN":[156],"models":[157],"classic":[159],"machine":[160],"learning":[161],"models.":[162],"paper":[164],"demonstrates":[165],"promising":[170],"competitive":[172],"in":[173],"contrast":[174],"common":[176],"beliefs.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}