{"id":"https://openalex.org/W7225796679","doi":"https://doi.org/10.5281/zenodo.24126718","title":"Intelligent Cybersecurity Threat Detection System: A Hybrid Machine Learning Approach for Known and Unknown Attacks","display_name":"Intelligent Cybersecurity Threat Detection System: A Hybrid Machine Learning Approach for Known and Unknown Attacks","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225796679","doi":"https://doi.org/10.5281/zenodo.24126718"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24126718","doi":"https://doi.org/10.5281/zenodo.24126718","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24126718","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24126718","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5131133243","display_name":"Yashasvi Sharma","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yashasvi Sharma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5161455540","display_name":"Amrish Kumar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amrish Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994999766349792,"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":0.9994999766349792,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":9.999999747378752e-05,"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":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6873999834060669},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6439999938011169},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6258999705314636},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6133000254631042},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5802000164985657},{"id":"https://openalex.org/C2779696439","wikidata":"https://www.wikidata.org/wiki/Q7512811","display_name":"Signature (topology)","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4797999858856201},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.46219998598098755},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C137524506","wikidata":"https://www.wikidata.org/wiki/Q2247688","display_name":"Anomaly-based intrusion detection system","level":3,"score":0.4406000077724457},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3797999918460846},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3788999915122986},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C22735295","wikidata":"https://www.wikidata.org/wiki/Q317671","display_name":"Botnet","level":3,"score":0.33869999647140503},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.337799996137619},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3249000012874603},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.3041999936103821},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.262800008058548},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.2563999891281128}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24126718","doi":"https://doi.org/10.5281/zenodo.24126718","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24126718","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.5281/zenodo.24126719","doi":"https://doi.org/10.5281/zenodo.24126719","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24126719","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24126718","doi":"https://doi.org/10.5281/zenodo.24126718","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24126718","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Signature-based":[0],"security":[1],"tools":[2],"detect":[3],"attacks":[4,133],"they":[5,172],"already":[6],"know":[7],"and":[8,27,53,79,112,119,134,142,191,198,207,213],"are":[9,93,199],"blind":[10],"to":[11,195],"zero-day":[12],"attacks,":[13,138,215],"while":[14],"many":[15],"machine":[16],"learning":[17],"intrusion":[18],"detection":[19,42],"systems":[20],"still":[21],"suffer":[22],"from":[23,51,84,167],"high":[24],"false-positive":[25],"rates":[26],"have":[28],"been":[29],"evaluated":[30],"mainly":[31],"on":[32,99,124],"static,":[33],"pre-recorded":[34],"datasets.":[35],"This":[36],"paper":[37],"presents":[38],"an":[39,58,108,120],"intelligent":[40],"threat":[41],"system":[43],"that":[44],"learns":[45],"normal":[46,66,125],"network":[47],"behaviour,":[48],"flags":[49],"deviations":[50],"it,":[52],"combines":[54],"supervised":[55],"classifiers":[56],"with":[57],"anomaly":[59],"detector.":[60],"A":[61],"synthetic":[62,169],"network-flow":[63],"generator":[64],"produces":[65],"traffic,":[67],"three":[68,80],"known":[69,132,212],"attack":[70,81],"types":[71,82],"(DoS":[72],"flood,":[73],"port":[74],"scan,":[75],"SSH":[76],"brute":[77],"force)":[78],"withheld":[83],"training":[85],"(Slowloris,":[86],"botnet":[87],"beacon,":[88],"data":[89],"exfiltration).":[90],"Four":[91],"detectors":[92],"compared":[94],"over":[95],"five":[96],"random":[97],"runs":[98],"18,000":[100],"test":[101],"flows:":[102],"signature":[103,145],"rules,":[104,146],"a":[105,113,148,184,205,217],"Random":[106,116],"Forest,":[107,117],"MLP":[109,118],"neural":[110],"network,":[111],"hybrid":[114,128],"of":[115,131,136,161,176],"Isolation":[121],"Forest":[122],"fitted":[123],"traffic.":[126],"The":[127,164,202],"detects":[129],"100%":[130],"68.4%":[135],"unseen":[137],"against":[139],"about":[140],"90%":[141],"0.1%":[143],"for":[144,210],"at":[147],"3.0%":[149],"false-alarm":[150],"rate.":[151],"Its":[152],"gain":[153],"is":[154,157,204],"uneven:":[155],"exfiltration":[156],"detected":[158],"only":[159],"5%":[160],"the":[162,174,177],"time.":[163],"results":[165],"come":[166],"author-generated":[168],"data,":[170],"so":[171],"show":[173],"behaviour":[175],"design":[178,206],"rather":[179],"than":[180],"field":[181],"performance.":[182],"CICIDS2017,":[183],"CNN-LSTM":[185],"model,":[186],"SHAP":[187],"explanations,":[188],"detection-latency":[189],"measurement":[190],"live":[192],"traffic":[193],"remain":[194],"be":[196],"done":[197],"not":[200,216],"claimed.":[201],"contribution":[203],"evaluation":[208],"framework":[209],"detecting":[211],"unknown":[214],"new":[218],"algorithm.":[219]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
