{"id":"https://openalex.org/W2782563033","doi":"https://doi.org/10.1109/iske.2017.8258845","title":"Complex-based optimization strategy for evasion attack","display_name":"Complex-based optimization strategy for evasion attack","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2782563033","doi":"https://doi.org/10.1109/iske.2017.8258845","mag":"2782563033"},"language":"en","primary_location":{"id":"doi:10.1109/iske.2017.8258845","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske.2017.8258845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","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/A5100404334","display_name":"Shu Li","orcid":"https://orcid.org/0000-0003-1742-2480"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shu Li","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, School of Computer Science, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, School of Computer Science, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076360288","display_name":"Yun Li","orcid":"https://orcid.org/0000-0002-2079-9484"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Li","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, School of Computer Science, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, School of Computer Science, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100404334"],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.4144,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68185509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"148","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":0.9997000098228455,"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":0.9997000098228455,"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":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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":0.7978216409683228},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6879959106445312},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6288397312164307},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6187363266944885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6152731776237488},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5653329491615295},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.45720842480659485},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3617645502090454},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.357275128364563},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33555665612220764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7978216409683228},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6879959106445312},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6288397312164307},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6187363266944885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6152731776237488},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5653329491615295},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.45720842480659485},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3617645502090454},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.357275128364563},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33555665612220764}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iske.2017.8258845","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske.2017.8258845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","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":15,"referenced_works":["https://openalex.org/W9657784","https://openalex.org/W1965052658","https://openalex.org/W2018061979","https://openalex.org/W2047237187","https://openalex.org/W2049652619","https://openalex.org/W2114296159","https://openalex.org/W2140691217","https://openalex.org/W2151298633","https://openalex.org/W2163918103","https://openalex.org/W2171074980","https://openalex.org/W2293768274","https://openalex.org/W2296452361","https://openalex.org/W2557044351","https://openalex.org/W3103836116","https://openalex.org/W4238255529"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W4285226279","https://openalex.org/W4387465941"],"abstract_inverted_index":{"Machine":[0],"learning":[1,19,39,49],"has":[2,60,121],"been":[3],"widely":[4],"used":[5],"in":[6,46,77,104,141],"security":[7],"related":[8],"applications,":[9],"such":[10],"as":[11,84,153],"spam":[12],"filter,":[13],"network":[14],"intrusion":[15],"detection.":[16,88],"In":[17,89],"machine":[18,48],"process,":[20],"the":[21,25,30,36,40,43,47,57,62,72,81,97,101,116,128,145,149,156,159,164,168],"test":[22,75,102],"set":[23,27],"and":[24,34,113],"training":[26,41],"usually":[28,52],"have":[29],"same":[31],"probability":[32],"distribution":[33],"through":[35],"information":[37],"of":[38,74,100,124,158,170],"set,":[42],"malicious":[44],"samples":[45,103],"algorithm":[50,59,108,129],"can":[51],"be":[53,131],"correctly":[54],"classified.":[55],"However,":[56],"classification":[58,63],"neglected":[61],"under":[64],"adversarial":[65,171],"environment,":[66],"so":[67,83,127,152],"instead":[68],"they":[69],"will":[70,93],"modify":[71,96,148],"features":[73],"data":[76],"order":[78,142],"to":[79,85,95,143,154],"spoof":[80],"classifier":[82],"escape":[86,155],"its":[87],"this":[90],"paper,":[91],"we":[92],"consider":[94],"feature":[98,120],"value":[99],"accordance":[105],"with":[106],"attack":[107],"proposed":[109],"by":[110],"Battista":[111],"Biggio":[112],"further":[114],"improve":[115,163],"algorithm.":[117],"As":[118],"each":[119],"a":[122,134],"range":[123],"independent":[125],"constraints,":[126],"should":[130],"transformed":[132],"into":[133],"constrained":[135],"optimization":[136],"problem.":[137],"This":[138],"is":[139],"done":[140],"make":[144],"original":[146],"sample":[147],"smaller":[150],"distance":[151],"detection":[157],"classifier,":[160],"while":[161],"also":[162],"convergence":[165],"rate":[166],"during":[167],"generation":[169],"samples.":[172]},"counts_by_year":[{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
