{"doi":"10.48550/arxiv.1811.04422","title":"Unknown","abstract":null,"journal":null,"year":null,"id":658082,"datarank":0.5019834103127706,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.1565956463636637,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.1565956463636637,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":5,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"An Optimal Control View of Adversarial Machine Learning","abstract":"I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary's goals to do harm and be hard to detect. This view encompasses many types of adversarial machine learning, including test-item attacks, training-data poisoning, and adversarial reward shaping. The view encourages adversarial machine learning researcher to utilize advances in control theory and reinforcement learning.","is_dataset_classified":null,"base_score":2.302585092994046,"endowment":2.302585092994046,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W2900159906","authors":[],"funders":[{"funder_name":"National Science Foundation","grant_id":"1545481","title":"NRT-DESE LUCID: A project-focused cross-disciplinary graduate training program for data-enabled research in human and machine learning and teaching"},{"funder_name":"National Science Foundation","grant_id":"1837132","title":"FMitF: Collaborative Research: Formal Methods for Machine Learning System Design"},{"funder_name":"National Science Foundation","grant_id":"1704117","title":"SHF: Medium: Formal Methods for Program Fairness"}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2019,"count":3},{"year":2020,"count":4},{"year":2021,"count":2}],"oa_status":"green","license":"arXiv Non-Exclusive Distribution","oa_locations":[{"url":"https://arxiv.org/pdf/1811.04422","host_type":"repository"},{"url":"http://arxiv.org/abs/1811.04422","host_type":"repository"},{"url":"https://doi.org/10.48550/arxiv.1811.04422","host_type":"repository"},{"url":"https://dx.doi.org/10.48550/arxiv.1811.04422","host_type":""}],"fields_of_study":["Adversarial Robustness in Machine Learning","Anomaly Detection Techniques and Applications","Advanced Bandit Algorithms Research","0202 electrical engineering, electronic engineering, information engineering","02 engineering and technology","01 natural sciences","0105 earth and related environmental sciences"],"mesh_terms":[],"keywords":["Adversarial system","Adversarial machine learning","Adversary","Computer science","Artificial intelligence","Reinforcement learning","Control (management)","Harm","Machine learning","Computer security","Psychology","Social psychology","FOS: Computer and information sciences","Computer Science - Machine Learning","Statistics - Machine Learning","Machine Learning (stat.ML)","Machine Learning (cs.LG)"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T03:16:31.730239Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}