{"id":"W2612030603","doi":"10.23919/date.2017.7927214","title":"Efficient drone hijacking detection using onboard motion sensors","year":2017,"lang":"en","type":"article","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China; U.S. Department of Homeland Security","keywords":"Drone; Computer science; Spoofing attack; Accelerometer; Global Positioning System; Gyroscope; Position (finance); Motion (physics); Artificial intelligence; Computer vision; Acceleration; Real-time computing; Computer security; Engineering; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001222997,0.0008685606,0.0005629065,0.0008218729,0.0002054552,0.0004085805,0.0005849283,0.0004835379,0.0006932723],"category_scores_gemma":[0.0007328343,0.000279877,0.0002362459,0.0004592836,0.0001764225,0.0007207759,0.0004910451,0.0003381607,0.0003591801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001744241,"about_ca_system_score_gemma":0.0002638519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002064964,"about_ca_topic_score_gemma":0.003764412,"domain_scores_codex":[0.999764,0.00003293135,0.00001201801,0.00005211005,0.0001057387,0.00003313748],"domain_scores_gemma":[0.9996943,0.00007540246,0.00008734926,0.0000432597,0.00008199445,0.00001762493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006085178,0.0002174745,0.01336047,0.0003902952,0.0001164254,0.0005510325,0.0003417214,0.06250026,0.3181733,0.001580316,0.002276063,0.5998841],"study_design_scores_gemma":[0.0000533159,0.0005089202,0.02428764,0.00006739857,0.00007540346,0.0006652343,0.0002069848,0.8582095,0.110736,0.000991019,0.004140062,0.0000584772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3323232,0.001242547,0.6584322,0.0001659509,0.0002112046,0.0001025813,0.0002561371,0.001656772,0.005609435],"genre_scores_gemma":[0.9046554,0.0005190533,0.09208795,0.00007028128,0.00004969877,0.00004863745,0.0001921956,0.00002832094,0.00234845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002064964,"threshold_uncertainty_score":0.004105866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222380012387021,"score_gpt":0.2382950771631077,"score_spread":0.2160712770392375,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}