{"id":"https://openalex.org/W4205398601","doi":"https://doi.org/10.1109/comsnets53615.2022.9668511","title":"Packet Batching for Reducing System Resource Consumption for Botnet Detection using Network Traffic Analysis","display_name":"Packet Batching for Reducing System Resource Consumption for Botnet Detection using Network Traffic Analysis","publication_year":2022,"publication_date":"2022-01-04","ids":{"openalex":"https://openalex.org/W4205398601","doi":"https://doi.org/10.1109/comsnets53615.2022.9668511"},"language":"en","primary_location":{"id":"doi:10.1109/comsnets53615.2022.9668511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets53615.2022.9668511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on COMmunication Systems &amp; NETworkS (COMSNETS)","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/A5056820286","display_name":"Himanshu Gandhi","orcid":null},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Himanshu Gandhi","raw_affiliation_strings":["Department of Computer Science and Engineering, IIT Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, IIT Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034236411","display_name":"Vinay J. Ribeiro","orcid":"https://orcid.org/0000-0001-5627-5343"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vinay Ribeiro","raw_affiliation_strings":["Department of Computer Science and Engineering, IIT Bombay, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, IIT Bombay, India","institution_ids":["https://openalex.org/I162827531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5056820286"],"corresponding_institution_ids":["https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":0.7128,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70348521,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998000264167786,"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":0.9998000264167786,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9988999962806702,"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/T11478","display_name":"Caching and Content Delivery","score":0.9980999827384949,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8396468162536621},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.701917827129364},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6345747113227844},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.5998557806015015},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45910170674324036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4504922926425934},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4481125771999359},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4291006326675415},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4240020215511322},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.4162842035293579},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3443463444709778},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.20375844836235046}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8396468162536621},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.701917827129364},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6345747113227844},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.5998557806015015},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45910170674324036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4504922926425934},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4481125771999359},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4291006326675415},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4240020215511322},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4162842035293579},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3443463444709778},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.20375844836235046},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/comsnets53615.2022.9668511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets53615.2022.9668511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on COMmunication Systems &amp; NETworkS (COMSNETS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2153356100","https://openalex.org/W2748868501","https://openalex.org/W2774161712","https://openalex.org/W2887289444","https://openalex.org/W2889228961","https://openalex.org/W2889557246","https://openalex.org/W2914113010","https://openalex.org/W2950865323","https://openalex.org/W2963674387","https://openalex.org/W2969580338","https://openalex.org/W2983872911","https://openalex.org/W3089037473","https://openalex.org/W3127369121","https://openalex.org/W3178193590","https://openalex.org/W3211183476","https://openalex.org/W4288839579","https://openalex.org/W4294284964","https://openalex.org/W6734710626","https://openalex.org/W6743493502","https://openalex.org/W6754230774","https://openalex.org/W6754496896","https://openalex.org/W6797184042"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175"],"abstract_inverted_index":{"The":[0,32],"Computer":[1],"architecture":[2],"research":[3,20],"extensively":[4],"studies":[5],"system":[6,44,108,127,164,188,209,238],"resource":[7,88,201,210],"consumption":[8,89,211],"by":[9,170,191],"algorithms":[10,175,193,199,246],"and":[11,43,54,62,68,120,130,146,151,163,167,194,237,244],"applications.":[12,259],"On":[13],"the":[14,37,107,126,155,171,177],"other":[15],"hand,":[16],"Machine":[17,98],"Learning":[18,99],"(ML)":[19],"focuses":[21],"on":[22,154,187,196,234],"obtaining":[23],"high":[24],"levels":[25],"of":[26,66,73,81,87,90,125,179,255],"accuracy":[27],"without":[28],"any":[29],"computational":[30],"constraint.":[31],"typical":[33],"approach":[34],"for":[35,39,46,76,134,200,251],"addressing":[36],"need":[38,100],"higher":[40],"compute":[41],"power":[42],"resources":[45,109,128,165,189],"ML":[47,74,91,118,174,192,198,245,257],"tasks":[48,94],"is":[49,113],"to":[50,101,115],"add":[51],"more":[52,252],"hardware":[53],"employ":[55],"lighter":[56],"frameworks":[57],"(e.g.,":[58],"using":[59],"TensorFlow":[60,67],"Lite":[61],"PyTorch":[63],"Mobile":[64],"instead":[65],"Pytorch,":[69],"respectively).":[70],"Extensive":[71],"use":[72],"models":[75,104],"applications,":[77],"especially":[78,133],"in":[79,123,176,182,215,224,231],"Internet":[80],"Things":[82],"(IoT)":[83],"security":[84],"requires":[85],"investigation":[86],"models.":[92],"Most":[93],"employing":[95,137],"Artificial":[96],"Intelligence/":[97],"choose":[102],"appropriate":[103,240],"judiciously":[105],"considering":[106],"consumed.":[110],"Therefore":[111],"it":[112],"required":[114],"investigate":[116],"various":[117],"techniques":[119],"benchmark":[121,145],"them":[122],"terms":[124],"(CPU":[129,166],"memory)":[131,168],"consumed,":[132],"IoT":[135,183],"applications":[136],"ML/":[138],"DL":[139],"methods.":[140],"In":[141],"this":[142],"work,":[143],"we":[144],"explore":[147],"network":[148],"packet":[149,241],"clustering":[150],"its":[152],"impact":[153],"trade-off":[156],"between":[157],"performance":[158],"metrics":[159],"(Accuracy,":[160],"F1":[161,225],"score)":[162],"consumed":[169,190],"commonly":[172],"used":[173],"context":[178,236],"botnet":[180],"detection":[181],"networks.":[184],"We":[185,206],"focus":[186],"not":[195],"optimising":[197],"constraints":[202],"or":[203],"application":[204,235],"workloads.":[205],"show":[207],"that":[208],"decreases":[212],"with":[213,218],"increase":[214],"aggregation":[216],"size,":[217],"at":[219],"least":[220],"20":[221],"%":[222],"improvement":[223],"scores":[226],"but":[227],"a":[228],"slight":[229],"reduction":[230],"accuracy.":[232],"Based":[233],"constraints,":[239],"batch":[242],"sizes":[243],"can":[247],"be":[248],"thus":[249],"chosen":[250],"rapid":[253],"prototyping":[254],"AI/":[256],"based":[258]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
