{"id":"https://openalex.org/W3206912558","doi":"https://doi.org/10.32604/cmc.2022.020044","title":"Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime","display_name":"Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime","publication_year":2021,"publication_date":"2021-10-11","ids":{"openalex":"https://openalex.org/W3206912558","doi":"https://doi.org/10.32604/cmc.2022.020044","mag":"3206912558"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2022.020044","doi":"https://doi.org/10.32604/cmc.2022.020044","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2022.020044","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2022.020044","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080231994","display_name":"Raniyah Wazirali","orcid":"https://orcid.org/0000-0002-3609-9351","observed_orcids":["https://orcid.org/0000-0002-3609-9351"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raniyah Wazirali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110623426","display_name":"Rami Ahmad","orcid":"https://orcid.org/0000-0003-3913-6397","observed_orcids":["https://orcid.org/0000-0003-3913-6397"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rami Ahmad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5110623426"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1018,"has_fulltext":false,"cited_by_count":56,"citation_normalized_percentile":{"value":0.94551607,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":87,"max":100},"biblio":{"volume":"70","issue":"3","first_page":"4922","last_page":"4946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11498","display_name":"Security in Wireless Sensor Networks","score":0.51419997215271,"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/T11498","display_name":"Security in Wireless Sensor Networks","score":0.51419997215271,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.46560001373291016,"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":0.006899999920278788,"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/denial-of-service-attacks","display_name":"denial-of-service attacks","score":0.7269999980926514},{"id":"https://openalex.org/keywords/ddos-attacks","display_name":"DDoS attacks","score":0.9440000057220459},{"id":"https://openalex.org/keywords/wireless-sensor-networks","display_name":"wireless sensor networks","score":0.8899999856948853},{"id":"https://openalex.org/keywords/intrusion-detection","display_name":"intrusion detection","score":0.6449999809265137},{"id":"https://openalex.org/keywords/network-lifetime","display_name":"network lifetime","score":0.6600000262260437},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.7710000276565552},{"id":"https://openalex.org/keywords/machine-learning-classification","display_name":"machine learning classification","score":0.5770000219345093},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"energy consumption","score":0.5870000123977661},{"id":"https://openalex.org/keywords/attack-traffic-classification","display_name":"attack traffic classification","score":0.42100000381469727}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8042339086532593},{"id":"https://openalex.org/C38822068","wikidata":"https://www.wikidata.org/wiki/Q131406","display_name":"Denial-of-service attack","level":3,"score":0.734920084476471},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6729907989501953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6655349731445312},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.616689920425415},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.5790164470672607},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5185980796813965},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.516869068145752},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.47787442803382874},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.47116073966026306},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4169911742210388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4043723940849304},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3416256308555603},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1319461166858673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0924246609210968},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2022.020044","doi":"https://doi.org/10.32604/cmc.2022.020044","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2022.020044","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2022.020044","doi":"https://doi.org/10.32604/cmc.2022.020044","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2022.020044","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1488038755","https://openalex.org/W1925165765","https://openalex.org/W1948188046","https://openalex.org/W1998768338","https://openalex.org/W2026245849","https://openalex.org/W2061903980","https://openalex.org/W2120685459","https://openalex.org/W2139460572","https://openalex.org/W2140877831","https://openalex.org/W2525336835","https://openalex.org/W2606537796","https://openalex.org/W2624994386","https://openalex.org/W2736413559","https://openalex.org/W2756518166","https://openalex.org/W2765706468","https://openalex.org/W2781133758","https://openalex.org/W2781902134","https://openalex.org/W2790239187","https://openalex.org/W2791790290","https://openalex.org/W2798501092","https://openalex.org/W2799942771","https://openalex.org/W2800577101","https://openalex.org/W2804867869","https://openalex.org/W2807619819","https://openalex.org/W2885821505","https://openalex.org/W2898382086","https://openalex.org/W2898761242","https://openalex.org/W2913865526","https://openalex.org/W2915969651","https://openalex.org/W2916641566","https://openalex.org/W2943221465","https://openalex.org/W2944491778","https://openalex.org/W2949228982","https://openalex.org/W2964224021","https://openalex.org/W2965568386","https://openalex.org/W2970328819","https://openalex.org/W2982682021","https://openalex.org/W2987793235","https://openalex.org/W2994950844","https://openalex.org/W2999988394","https://openalex.org/W3003565393","https://openalex.org/W3012437309","https://openalex.org/W3015203156","https://openalex.org/W3016371023","https://openalex.org/W3036962952","https://openalex.org/W3048576163","https://openalex.org/W3084386044","https://openalex.org/W3088249606","https://openalex.org/W3091198356","https://openalex.org/W3128258093","https://openalex.org/W6741693005"],"related_works":["https://openalex.org/W4300828025","https://openalex.org/W4406838235","https://openalex.org/W4385627240","https://openalex.org/W4404371055","https://openalex.org/W4412185399","https://openalex.org/W2035590597","https://openalex.org/W4390643483","https://openalex.org/W4410158195","https://openalex.org/W4409771096","https://openalex.org/W3182114900"],"abstract_inverted_index":{"Energy":[0],"and":[1,23,51,87,129,146,157,193,215,263],"security":[2,33,49],"remain":[3],"the":[4,30,52,62,75,78,97,144,151,158,162,194,199,203,227,242,247,252,255,264,277,284,289],"main":[5],"two":[6],"challenges":[7],"in":[8,46,83,136,179,211,251,294],"Wireless":[9],"Sensor":[10],"Networks":[11],"(WSNs).":[12],"Therefore,":[13],"protecting":[14],"these":[15,67,180],"WSN":[16,31,84,91,290],"networks":[17,32],"from":[18,258],"Denial":[19],"of":[20,29,69,77,100,171,229,254,286],"Service":[21],"(DoS)":[22],"Distributed":[24],"DoS":[25],"(DDoS)":[26],"is":[27,268],"one":[28],"tasks.":[34],"Traditional":[35],"packet":[36],"deep":[37],"scan":[38],"systems":[39,61],"that":[40,143,240],"rely":[41],"on":[42,90,133,153,169,288],"open":[43,53],"field":[44,54],"inspection":[45],"transport":[47],"layer":[48],"packets":[50],"encryption":[55],"trend":[56],"are":[57,250],"making":[58],"machine":[59,80,102],"learning-based":[60],"only":[63],"viable":[64],"choice":[65],"for":[66,226,241,273],"types":[68],"attacks.":[70],"This":[71],"paper":[72,238,282],"contributes":[73],"to":[74,166,260,299],"evaluation":[76],"use":[79],"learning":[81,103],"algorithms":[82,168],"nodes":[85],"traffic":[86],"their":[88],"effect":[89,285],"network":[92],"life":[93],"time.":[94,197],"We":[95],"examined":[96],"performance":[98,164,173,176],"metrics":[99,177],"different":[101,137,167],"classification":[104,148],"categories":[105,149,267],"such":[106],"as":[107],"K-Nearest":[108],"Neighbour":[109],"(KNN),":[110],"Logistic":[111],"Regression":[112],"(LR),":[113],"Support":[114],"Vector":[115],"Machine":[116],"(SVM),":[117],"Gboost,":[118],"Decision":[119],"Tree":[120],"(DT),":[121],"Na\u00efve":[122],"Bayes,":[123],"Long":[124],"Short":[125],"Term":[126],"Memory":[127],"(LSTM),":[128],"Multi-Layer":[130],"Perceptron":[131],"(MLP)":[132],"a":[134,295],"WSN-dataset":[135],"sizes.":[138],"The":[139,175],"test":[140,200],"results":[141,201,249],"proved":[142],"statistical":[145,155,244],"logical":[147],"performed":[150],"best":[152,163,248],"numeric":[154,243],"datasets,":[156],"Gboost":[159,204,287],"algorithm":[160,205],"showed":[161,202],"compared":[165,298],"average":[170,228],"all":[172,230],"metrics.":[174],"used":[178],"validations":[181],"were":[182],"accuracy,":[183,212],"F1-score,":[184,213],"False":[185,189],"Positive":[186],"Ratio":[187,191],"(FPR),":[188],"Negative":[190],"(FNR),":[192],"training":[195,219,231],"execution":[196,220,233],"Moreover,":[198],"got":[206],"99.6%,":[207],"98.8%,":[208],"0.4%":[209],"0.13%":[210],"FPR,":[214],"FNR,":[216],"respectively.":[217],"At":[218],"time,":[221],"it":[222],"obtained":[223],"1.41":[224],"s":[225],"time":[232],"datasets.":[234],"In":[235],"addition,":[236],"this":[237,281],"demonstrated":[239],"data":[245],"type,":[246],"size":[253],"dataset":[256],"ranging":[257],"3000":[259],"6000":[261],"records":[262],"percentage":[265],"between":[266],"not":[269],"less":[270],"than":[271],"50%":[272],"each":[274],"category":[275],"with":[276],"other":[278,300],"categories.":[279],"Furthermore,":[280],"investigated":[283],"lifetime,":[291],"which":[292],"resulted":[293],"32%":[296],"reduction":[297],"Gboost-free":[301],"scenarios.":[302]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-10-25T00:00:00"}