{"id":"https://openalex.org/W2557513839","doi":"https://doi.org/10.1007/978-3-319-50127-7_11","title":"Deep Learning for Classification of Malware System Call Sequences","display_name":"Deep Learning for Classification of Malware System Call Sequences","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2557513839","doi":"https://doi.org/10.1007/978-3-319-50127-7_11","mag":"2557513839"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-319-50127-7_11","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-50127-7_11","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/978-3-319-50127-7_11","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080832826","display_name":"Bojan Kolosnjaji","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Bojan Kolosnjaji","raw_affiliation_strings":["Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065154871","display_name":"Apostolis Zarras","orcid":"https://orcid.org/0000-0002-8480-6989"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Apostolis Zarras","raw_affiliation_strings":["Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037134897","display_name":"George D. Webster","orcid":"https://orcid.org/0000-0002-1591-8952"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"George Webster","raw_affiliation_strings":["Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073408463","display_name":"Claudia Eckert","orcid":"https://orcid.org/0000-0002-2201-3828"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Claudia Eckert","raw_affiliation_strings":["Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5080832826"],"corresponding_institution_ids":["https://openalex.org/I62916508"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":54.2828,"has_fulltext":false,"cited_by_count":504,"citation_normalized_percentile":{"value":0.99889503,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"137","last_page":"149"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9916999936103821,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9799000024795532,"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/computer-science","display_name":"Computer science","score":0.8831837177276611},{"id":"https://openalex.org/keywords/malware","display_name":"Malware","score":0.808017373085022},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.755843997001648},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6742526292800903},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5830167531967163},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5560382008552551},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5400699973106384},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5146201848983765},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5111232399940491},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42665913701057434}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8831837177276611},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.808017373085022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.755843997001648},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6742526292800903},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5830167531967163},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5560382008552551},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5400699973106384},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5146201848983765},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5111232399940491},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42665913701057434},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/978-3-319-50127-7_11","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-50127-7_11","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},{"id":"pmh:oai:cris.maastrichtuniversity.nl:openaire_cris_publications/7a970830-42f7-4442-9967-fa2c8987af42","is_oa":false,"landing_page_url":"https://cris.maastrichtuniversity.nl/en/publications/7a970830-42f7-4442-9967-fa2c8987af42","pdf_url":null,"source":{"id":"https://openalex.org/S4306402616","display_name":"Research Publications (Maastricht University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I34352273","host_organization_name":"Maastricht University","host_organization_lineage":["https://openalex.org/I34352273"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Kolosnjaji , B , Zarras , A , Webster , G &amp; Eckert , C 2016 , Deep Learning for Classification of Malware System Call Sequences . in Proceedings of the 29th Australasian Joint Conference on Artificial Intelligence (AI) . https://doi.org/10.1007/978-3-319-50127-7_11","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"doi:10.1007/978-3-319-50127-7_11","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-50127-7_11","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1535668279","https://openalex.org/W1545528966","https://openalex.org/W1581009051","https://openalex.org/W1673310716","https://openalex.org/W1893133781","https://openalex.org/W1910686388","https://openalex.org/W1966948031","https://openalex.org/W2006875630","https://openalex.org/W2013586455","https://openalex.org/W2057079516","https://openalex.org/W2072128103","https://openalex.org/W2072698166","https://openalex.org/W2087740020","https://openalex.org/W2095705004","https://openalex.org/W2129860818","https://openalex.org/W2135143063","https://openalex.org/W2138644293","https://openalex.org/W2144112223","https://openalex.org/W2152175008","https://openalex.org/W2157949690","https://openalex.org/W2167277498","https://openalex.org/W2186294736","https://openalex.org/W2187089797","https://openalex.org/W2234884273","https://openalex.org/W2271840356","https://openalex.org/W2476429474","https://openalex.org/W2493100395","https://openalex.org/W2510021091","https://openalex.org/W4231109964","https://openalex.org/W6815970472"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W2097492617","https://openalex.org/W2753240997","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"The":[0],"increase":[1],"in":[2,14,43,85,155,193],"number":[3,109],"and":[4,17,70,114,151,206],"variety":[5],"of":[6,19,39,60,102,110,140,176,202],"malware":[7,21,133,141,194],"samples":[8],"amplifies":[9],"the":[10,20,37,54,100,132,138,159],"need":[11,38],"for":[12,137,162],"improvement":[13,97],"automatic":[15],"detection":[16],"classification":[18,83],"variants.":[22],"Machine":[23],"learning":[24,63],"is":[25,115],"a":[26,75,107,145,168],"natural":[27,93],"choice":[28],"to":[29,53,125,130,157,198],"cope":[30],"with":[31,106,178],"this":[32,121,211],"increase,":[33],"because":[34],"it":[35],"addresses":[36],"discovering":[40],"underlying":[41],"patterns":[42],"large-scale":[44],"datasets.":[45],"Nowadays,":[46],"neural":[47,77,104,146,213],"network":[48,147,153,214],"methodology":[49],"has":[50],"been":[51],"grown":[52],"state":[55],"that":[56,173,186],"can":[57,79],"surpass":[58],"limitations":[59],"previous":[61],"machine":[62],"methods,":[64],"such":[65,88],"as":[66,89,117],"Hidden":[67],"Markov":[68],"Models":[69],"Support":[71],"Vector":[72],"Machines.":[73],"As":[74],"consequence,":[76],"networks":[78,105],"now":[80],"offer":[81],"superior":[82],"accuracy":[84],"many":[86],"domains,":[87],"computer":[90],"vision":[91],"or":[92],"language":[94],"processing.":[95],"This":[96,164],"comes":[98],"from":[99],"possibility":[101],"constructing":[103],"higher":[108],"potentially":[111],"diverse":[112],"layers":[113,154],"known":[116],"Deep":[118],"Learning.":[119],"In":[120],"paper,":[122],"we":[123,166],"attempt":[124],"transfer":[126],"these":[127],"performance":[128],"improvements":[129],"model":[131],"system":[134],"call":[135],"sequences":[136],"purpose":[139],"classification.":[142,163],"We":[143],"construct":[144],"based":[148],"on":[149,204,208],"convolutional":[150],"recurrent":[152],"order":[156],"obtain":[158],"best":[160],"features":[161],"way":[165],"get":[167],"hierarchical":[169],"feature":[170],"extraction":[171],"architecture":[172],"combines":[174],"convolution":[175],"n-grams":[177],"full":[179],"sequential":[180],"modeling.":[181],"Our":[182],"evaluation":[183],"results":[184],"demonstrate":[185],"our":[187],"approach":[188],"outperforms":[189],"previously":[190],"used":[191],"methods":[192],"classification,":[195],"being":[196],"able":[197],"achieve":[199],"an":[200],"average":[201],"85.6%":[203],"precision":[205],"89.4%":[207],"recall":[209],"using":[210],"combined":[212],"architecture.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":18},{"year":2025,"cited_by_count":40},{"year":2024,"cited_by_count":37},{"year":2023,"cited_by_count":46},{"year":2022,"cited_by_count":48},{"year":2021,"cited_by_count":97},{"year":2020,"cited_by_count":80},{"year":2019,"cited_by_count":89},{"year":2018,"cited_by_count":33},{"year":2017,"cited_by_count":16}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
