{"id":"https://openalex.org/W4309345946","doi":"https://doi.org/10.1109/cns56114.2022.9947234","title":"ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning","display_name":"ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning","publication_year":2022,"publication_date":"2022-10-03","ids":{"openalex":"https://openalex.org/W4309345946","doi":"https://doi.org/10.1109/cns56114.2022.9947234"},"language":"en","primary_location":{"id":"doi:10.1109/cns56114.2022.9947234","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cns56114.2022.9947234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Conference on Communications and Network Security (CNS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vtechworks.lib.vt.edu/bitstreams/2a4f3700-ecfb-48b9-88d0-cfe41cbeba9d/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100758311","display_name":"Haider Ali","orcid":"https://orcid.org/0009-0008-0851-0394"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haider Ali","raw_affiliation_strings":["Computer Science, Virginia Tech,VA","Computer Science, Virginia Tech, VA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Virginia Tech,VA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Computer Science, Virginia Tech, VA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042187047","display_name":"Mohannad Al Ameedi","orcid":"https://orcid.org/0009-0000-9313-4910"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohannad Al Ameedi","raw_affiliation_strings":["Computer Science, Virginia Tech,VA","Computer Science, Virginia Tech, VA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Virginia Tech,VA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Computer Science, Virginia Tech, VA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103191815","display_name":"Ananthram Swami","orcid":"https://orcid.org/0000-0003-1439-332X"},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ananthram Swami","raw_affiliation_strings":["Army Research Laboratory,MD,USA","Army Research Laboratory, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Army Research Laboratory,MD,USA","institution_ids":["https://openalex.org/I166416128"]},{"raw_affiliation_string":"Army Research Laboratory, MD, USA","institution_ids":["https://openalex.org/I166416128"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061056153","display_name":"Rui Ning","orcid":"https://orcid.org/0000-0003-4050-6252"},"institutions":[{"id":"https://openalex.org/I4210138378","display_name":"Dominion (United States)","ror":"https://ror.org/038q98v71","country_code":"US","type":"company","lineage":["https://openalex.org/I4210138378"]},{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Ning","raw_affiliation_strings":["Old Dominion University,Computer Science,VA,USA","Computer Science, Old Dominion University, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Old Dominion University,Computer Science,VA,USA","institution_ids":["https://openalex.org/I4210138378","https://openalex.org/I81365321"]},{"raw_affiliation_string":"Computer Science, Old Dominion University, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392380","display_name":"Jiang Li","orcid":"https://orcid.org/0000-0003-0091-6986"},"institutions":[{"id":"https://openalex.org/I4210138378","display_name":"Dominion (United States)","ror":"https://ror.org/038q98v71","country_code":"US","type":"company","lineage":["https://openalex.org/I4210138378"]},{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiang Li","raw_affiliation_strings":["Old Dominion University,Electrical Engineering,VA,USA","Electrical Engineering, Old Dominion University, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Old Dominion University,Electrical Engineering,VA,USA","institution_ids":["https://openalex.org/I4210138378","https://openalex.org/I81365321"]},{"raw_affiliation_string":"Electrical Engineering, Old Dominion University, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101816294","display_name":"Hongyi Wu","orcid":"https://orcid.org/0000-0002-6112-9313"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongyi Wu","raw_affiliation_strings":["The University of Arizona,Electrical and Computer Engineering,AZ,USA","Electrical and Computer Engineering, The University of Arizona, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Arizona,Electrical and Computer Engineering,AZ,USA","institution_ids":["https://openalex.org/I138006243"]},{"raw_affiliation_string":"Electrical and Computer Engineering, The University of Arizona, AZ, USA","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011649304","display_name":"Jin-Hee Cho","orcid":"https://orcid.org/0000-0002-5908-4662"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jin-Hee Cho","raw_affiliation_strings":["Computer Science, Virginia Tech,VA","Computer Science, Virginia Tech, VA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Virginia Tech,VA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Computer Science, Virginia Tech, VA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9549999833106995,"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.9218999743461609,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.9050140380859375},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8511923551559448},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6654260158538818},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6189500093460083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5546497106552124},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5162713527679443},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4888400435447693},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3660206198692322}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.9050140380859375},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8511923551559448},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6654260158538818},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6189500093460083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5546497106552124},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5162713527679443},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4888400435447693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3660206198692322}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cns56114.2022.9947234","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cns56114.2022.9947234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Conference on Communications and Network Security (CNS)","raw_type":"proceedings-article"},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/113063","is_oa":true,"landing_page_url":"http://hdl.handle.net/10919/113063","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/2a4f3700-ecfb-48b9-88d0-cfe41cbeba9d/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/113063","is_oa":true,"landing_page_url":"http://hdl.handle.net/10919/113063","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/2a4f3700-ecfb-48b9-88d0-cfe41cbeba9d/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[{"score":0.8399999737739563,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G215133263","display_name":"III: Medium: Collaborative Research: MUDL: Multidimensional Uncertainty-Aware Deep Learning Framework","funder_award_id":"2107450","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4309345946.pdf","grobid_xml":"https://content.openalex.org/works/W4309345946.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1945616565","https://openalex.org/W2145339207","https://openalex.org/W2616841723","https://openalex.org/W2640329709","https://openalex.org/W2773525213","https://openalex.org/W2774644650","https://openalex.org/W2911506106","https://openalex.org/W2941205169","https://openalex.org/W2949103145","https://openalex.org/W2962847335","https://openalex.org/W2963178695","https://openalex.org/W2963542245","https://openalex.org/W2963744840","https://openalex.org/W2963857521","https://openalex.org/W2964272011","https://openalex.org/W2997293639","https://openalex.org/W3009542902","https://openalex.org/W3016549807","https://openalex.org/W3098237412","https://openalex.org/W3109751798","https://openalex.org/W3124446631","https://openalex.org/W4287692319","https://openalex.org/W4288029431","https://openalex.org/W4289752760","https://openalex.org/W4293846201","https://openalex.org/W4298857966","https://openalex.org/W6637967152","https://openalex.org/W6640425456","https://openalex.org/W6719080892","https://openalex.org/W6733049761","https://openalex.org/W6737897983","https://openalex.org/W6738693630","https://openalex.org/W6747027214","https://openalex.org/W6752420067","https://openalex.org/W6753549322","https://openalex.org/W6770271268","https://openalex.org/W6774469542","https://openalex.org/W6774966973","https://openalex.org/W6781541518","https://openalex.org/W6786604383","https://openalex.org/W6789484704"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Existing":[0],"adversarial":[1,30,42,61],"algorithms":[2],"for":[3],"Deep":[4,49,107],"Reinforcement":[5],"Learning":[6,121],"(DRL)":[7],"have":[8,100],"largely":[9],"focused":[10],"on":[11,70],"identifying":[12],"an":[13],"optimal":[14],"time":[15],"to":[16,63],"attack":[17],"a":[18,37,54,166],"DRL":[19,33,41,66,92,112,118,149],"agent.":[20],"However,":[21],"little":[22],"work":[23],"has":[24],"been":[25,102],"explored":[26],"in":[27,32,104,148],"injecting":[28],"efficient":[29,57],"perturbations":[31],"environments.":[34],"We":[35,114],"propose":[36],"suite":[38],"of":[39,56,73,91,97,169,190],"novel":[40,71,95],"attacks,":[43],"called":[44],"ACADIA,":[45],"representing":[46],"AttaCks":[47],"Against":[48],"reInforcement":[50],"leArning.":[51],"ACADIA":[52,160,172],"provides":[53],"set":[55],"and":[58,86,111,124,132,137,152],"robust":[59],"perturbation-based":[60],"attacks":[62,93,139],"disturb":[64],"the":[65,105,145,158,178],"agent's":[67],"decision-making":[68],"based":[69],"combinations":[72],"techniques":[74,99],"utilizing":[75],"momentum,":[76],"ADAM":[77],"optimizer":[78],"(i.e.,":[79,150],"Root":[80],"Mean":[81],"Square":[82],"Propagation,":[83],"or":[84,143],"RMSProp),":[85],"initial":[87],"randomization.":[88],"These":[89],"kinds":[90],"with":[94,142,185],"integration":[96],"such":[98],"not":[101],"studied":[103],"existing":[106,162],"Neural":[108],"Networks":[109],"(DNNs)":[110],"research.":[113],"consider":[115],"two":[116],"well-known":[117],"algorithms,":[119],"Deep-Q":[120],"Network":[122],"(DQN)":[123],"Proximal":[125],"Policy":[126],"Optimization":[127],"(PPO),":[128],"under":[129,165,188],"Atari":[130],"games":[131],"MuJoCo":[133],"where":[134],"both":[135],"targeted":[136],"non-targeted":[138],"are":[140],"considered":[141],"without":[144],"state-of-the-art":[146,179],"defenses":[147,189],"RADIAL":[151],"ATLA).":[153],"Our":[154],"results":[155],"demonstrate":[156],"that":[157],"proposed":[159],"outperforms":[161],"gradient-based":[163],"counterparts":[164],"wide":[167],"range":[168],"experimental":[170],"settings.":[171],"is":[173],"nine":[174],"times":[175],"faster":[176],"than":[177],"Carlini":[180],"&":[181],"Wagner":[182],"(CW)":[183],"method":[184],"better":[186],"performance":[187],"DRL.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
