{"id":"https://openalex.org/W3117997274","doi":"https://doi.org/10.1145/3430984.3431036","title":"Robust Detection of Network Intrusion using Tree-based Convolutional Neural Networks","display_name":"Robust Detection of Network Intrusion using Tree-based Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-12-28","ids":{"openalex":"https://openalex.org/W3117997274","doi":"https://doi.org/10.1145/3430984.3431036","mag":"3117997274"},"language":"en","primary_location":{"id":"doi:10.1145/3430984.3431036","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3430984.3431036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd ACM India Joint International Conference on Data Science &amp; Management of Data (8th ACM IKDD CODS &amp; 26th COMAD)","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/A5069851219","display_name":"Sanket Mishra","orcid":"https://orcid.org/0000-0002-3193-8160"},"institutions":[{"id":"https://openalex.org/I4210101034","display_name":"Birla Institute of Technology and Science - Hyderabad Campus","ror":"https://ror.org/014ctt859","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210101034","https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sanket Mishra","raw_affiliation_strings":["BITS Pilani - Hyderabad Campus, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BITS Pilani - Hyderabad Campus, India","institution_ids":["https://openalex.org/I4210101034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062717710","display_name":"Rohit Dwivedula","orcid":"https://orcid.org/0000-0002-2132-5707"},"institutions":[{"id":"https://openalex.org/I4210101034","display_name":"Birla Institute of Technology and Science - Hyderabad Campus","ror":"https://ror.org/014ctt859","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210101034","https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rohit Dwivedula","raw_affiliation_strings":["BITS Pilani - Hyderabad Campus, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BITS Pilani - Hyderabad Campus, India","institution_ids":["https://openalex.org/I4210101034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063912273","display_name":"Vedanti Kshirsagar","orcid":"https://orcid.org/0000-0001-9178-9219"},"institutions":[{"id":"https://openalex.org/I4210101034","display_name":"Birla Institute of Technology and Science - Hyderabad Campus","ror":"https://ror.org/014ctt859","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210101034","https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Varad Kshirsagar","raw_affiliation_strings":["BITS Pilani - Hyderabad Campus, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BITS Pilani - Hyderabad Campus, India","institution_ids":["https://openalex.org/I4210101034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063245652","display_name":"Chittaranjan Hota","orcid":"https://orcid.org/0000-0002-6031-6408"},"institutions":[{"id":"https://openalex.org/I4210101034","display_name":"Birla Institute of Technology and Science - Hyderabad Campus","ror":"https://ror.org/014ctt859","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210101034","https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chittaranjan Hota","raw_affiliation_strings":["BITS Pilani - Hyderabad Campus, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BITS Pilani - Hyderabad Campus, India","institution_ids":["https://openalex.org/I4210101034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9724,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.78996985,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"233","last_page":"237"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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":1.0,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9984999895095825,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8144285678863525},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7602999210357666},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6789566874504089},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49121326208114624},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.48865607380867004},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4281070828437805},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3937494456768036},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3828669786453247},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3700319528579712},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.063478022813797}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8144285678863525},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7602999210357666},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6789566874504089},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49121326208114624},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.48865607380867004},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4281070828437805},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3937494456768036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3828669786453247},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3700319528579712},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.063478022813797},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3430984.3431036","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3430984.3431036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd ACM India Joint International Conference on Data Science &amp; Management of Data (8th ACM IKDD CODS &amp; 26th COMAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1444952417","https://openalex.org/W1995678176","https://openalex.org/W2061438946","https://openalex.org/W2099940443","https://openalex.org/W2106411961","https://openalex.org/W2113207845","https://openalex.org/W2734779510","https://openalex.org/W2762776925","https://openalex.org/W2766315530","https://openalex.org/W2786516067","https://openalex.org/W2799700280","https://openalex.org/W2889950218","https://openalex.org/W2902455138","https://openalex.org/W2911409473","https://openalex.org/W2914730628","https://openalex.org/W2928016897","https://openalex.org/W2963261650","https://openalex.org/W2965383240","https://openalex.org/W2973862992","https://openalex.org/W2974859516","https://openalex.org/W2984419450","https://openalex.org/W2991722918","https://openalex.org/W2996977448","https://openalex.org/W3005805195"],"related_works":["https://openalex.org/W3093612317","https://openalex.org/W4287776258","https://openalex.org/W3081496756","https://openalex.org/W3027997911","https://openalex.org/W2175746458","https://openalex.org/W2732542196","https://openalex.org/W2760085659","https://openalex.org/W2738221750","https://openalex.org/W3012978760","https://openalex.org/W2912288872"],"abstract_inverted_index":{"Automated":[0],"Intrusion":[1],"Detection":[2],"Systems":[3],"(IDS)":[4],"are":[5,38],"the":[6,30,84,95,105,123,132],"first":[7],"line":[8],"of":[9,23,32,57,97,108,114,122,144],"defense":[10],"that":[11,37,75],"monitor":[12],"network":[13,34],"activity":[14],"to":[15,29,40,47,82],"profile":[16],"and":[17,86,116,127,146,150],"identify":[18,83],"suspicious":[19],"activity.":[20],"This":[21],"detection":[22],"intrusion":[24],"is":[25],"further":[26],"complicated":[27],"due":[28],"emergence":[31],"sophisticated":[33],"based":[35],"attacks":[36,53,85],"difficult":[39],"identify.":[41],"Deep":[42],"learning":[43,126,129],"approaches":[44],"have":[45],"proven":[46],"be":[48,77],"effective":[49],"in":[50,60],"isolating":[51],"such":[52],"through":[54],"efficient":[55],"identification":[56],"non-linear":[58],"relationships":[59],"data.":[61],"In":[62],"this":[63],"work,":[64],"we":[65],"propose":[66],"a":[67,138],"hierarchical":[68],"Convolutional":[69],"Neural":[70],"Network":[71],"approach":[72,102],",":[73],"TreeNets,":[74],"can":[76],"used":[78],"as":[79],"an":[80,142],"IDS":[81],"segregate":[87],"them":[88],"into":[89],"binary":[90],"outcomes.":[91],"The":[92],"paper":[93],"depicts":[94],"usage":[96],"Binary":[98],"Grey":[99],"Wolf":[100],"Optimization":[101],"for":[103],"identifying":[104],"optimal":[106],"set":[107],"features.":[109],"We":[110],"exhibit":[111],"three":[112],"variants":[113],"TreeNets":[115],"compare":[117],"their":[118],"performance":[119,140],"against":[120],"state":[121],"art":[124],"machine":[125],"deep":[128],"models":[130],"on":[131,148],"NSLKDD":[133],"dataset.":[134],"Experimental":[135],"results":[136],"depict":[137],"competitive":[139],"with":[141],"accuracy":[143],"82.16%":[145],"66.37%":[147],"KDDTest+":[149],"KDD-Test-21":[151],"respectively.":[152]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
