{"id":"https://openalex.org/W7118657847","doi":"https://doi.org/10.48550/arxiv.2601.01289","title":"dataRLsec: Safety, Security, and Reliability With Robust Offline Reinforcement Learning for DPAs","display_name":"dataRLsec: Safety, Security, and Reliability With Robust Offline Reinforcement Learning for DPAs","publication_year":2026,"publication_date":"2026-01-03","ids":{"openalex":"https://openalex.org/W7118657847","doi":"https://doi.org/10.48550/arxiv.2601.01289"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.01289","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01289","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.01289","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121590919","display_name":"Shriram KS Pandian","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pandian, Shriram KS","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5040347462","display_name":"Naresh Kshetri","orcid":"https://orcid.org/0000-0002-3282-7331"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kshetri, Naresh","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5121590919"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.6011999845504761,"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":0.6011999845504761,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.07180000096559525,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.06069999933242798,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7408999800682068},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6098999977111816},{"id":"https://openalex.org/keywords/hacker","display_name":"Hacker","score":0.5392000079154968},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.44130000472068787},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3610999882221222},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.3571000099182129}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7408999800682068},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6783999800682068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6561999917030334},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6098999977111816},{"id":"https://openalex.org/C86844869","wikidata":"https://www.wikidata.org/wiki/Q2798820","display_name":"Hacker","level":2,"score":0.5392000079154968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5205000042915344},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.44130000472068787},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3571000099182129},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.329800009727478},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.29649999737739563},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.01289","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01289","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.01289","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01289","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4824504852294922}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Data":[0],"poisoning":[1,126],"attacks":[2,128],"(DPAs)":[3],"are":[4,31,51,181,198],"becoming":[5],"popular":[6,125,131],"as":[7],"artificial":[8,23],"intelligence":[9,24],"(AI)":[10,25],"algorithms,":[11,15],"machine":[12],"learning":[13,18,68,140],"(ML)":[14],"and":[16,28,56,75,104,119,146,177,189,195,213],"deep":[17,66],"(DL)":[19],"algorithms":[20],"in":[21,36,41,91,121],"this":[22],"era.":[26],"Hackers":[27],"penetration":[29],"testers":[30],"excessively":[32],"injecting":[33],"malicious":[34],"contents":[35],"the":[37,73,86,92,100,106,117,144,164,168,171,174,178,201],"training":[38],"data":[39,43,123,127,132,208],"(and":[40],"testing":[42],"too)":[44],"that":[45,50],"leads":[46],"to":[47,54,69,129,185,203,211],"false":[48],"results":[49],"very":[52],"hard":[53],"inspect":[55],"predict.":[57],"We":[58,110,134],"have":[59,111,135],"analyzed":[60,112],"several":[61,113],"recent":[62],"technologies":[63],"used":[64],"(from":[65],"reinforcement":[67,139],"federated":[70],"learning)":[71],"for":[72,143,191],"DPAs":[74,118],"their":[76],"safety,":[77,188],"security,":[78],"&":[79],"countermeasures.":[80],"The":[81,160,193],"problem":[82,87],"setup":[83],"along":[84,152],"with":[85,95,116,148,153,183,200,206],"estimation":[88],"is":[89,102,108],"shown":[90],"MuJoCo":[93],"environment":[94],"performance":[96],"of":[97,163],"HalfCheetah":[98],"before":[99],"dataset":[101,107],"poisoned":[103],"after":[105],"poisoned.":[109],"risks":[114],"associated":[115],"falsification":[120],"medical":[122],"from":[124],"some":[130],"defenses.":[133],"proposed":[136,165],"robust":[137],"offline":[138,186],"(Offline":[141],"RL)":[142],"safety":[145],"reliability":[147],"weighted":[149,155],"hash":[150],"verification":[151],"density-ratio":[154],"behavioral":[156],"cloning":[157],"(DWBC)":[158],"algorithm.":[159],"four":[161],"stages":[162],"algorithm":[166],"(as":[167],"Stage":[169,172,175,179],"0,":[170],"1,":[173],"2,":[176],"3)":[180],"described":[182],"respect":[184],"RL,":[187],"security":[190],"DPAs.":[192],"conclusion":[194],"future":[196,215],"scope":[197],"provided":[199],"intent":[202],"combine":[204],"DWBC":[205],"other":[207],"defense":[209],"strategies":[210],"counter":[212],"protect":[214],"contamination":[216],"cyberattacks.":[217]},"counts_by_year":[],"updated_date":"2026-01-08T20:10:11.968330","created_date":"2026-01-08T00:00:00"}