{"id":"W2125624394","doi":"","title":"Targeted threat index: characterizing and quantifying politically-motivated targeted malware","year":2014,"lang":"en","type":"article","venue":"USENIX Security Symposium","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Malware; Sophistication; Computer security; Compromise; Computer science; Internet privacy; Social engineering (security); Metric (unit); Business; Political science; Sociology; Marketing","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":[0.001319702,0.000541348,0.0002196432,0.005823871,0.0005411751,0.0008951469,0.0003118321,0.0005382967,0.0009685978],"category_scores_gemma":[0.008875549,0.0001131466,0.0003252289,0.001792763,0.0005790322,0.001321901,0.0008331001,0.0004639974,0.000278813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007592533,"about_ca_system_score_gemma":0.0004713882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001621654,"about_ca_topic_score_gemma":0.002036681,"domain_scores_codex":[0.9988702,0.0002679381,0.00009202891,0.00009677318,0.0005618599,0.0001111475],"domain_scores_gemma":[0.987793,0.004032509,0.005676238,0.0006389736,0.001234399,0.0006248511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001338818,0.0004588068,0.8897708,0.0002102752,0.0001516608,0.0003865279,0.002792396,0.01074397,0.01456242,0.004297981,0.001683349,0.07480795],"study_design_scores_gemma":[0.000005809623,0.0004388617,0.9458966,0.00003469357,0.00004302775,0.0007949162,0.001577276,0.04107269,0.004956951,0.002014596,0.003106168,0.00005841331],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868184,0.0001184369,0.005894163,0.00009074849,0.00001023554,0.0001494252,0.0005184748,0.00009950997,0.006300627],"genre_scores_gemma":[0.9951394,0.00007095274,0.003595741,0.00002614465,0.00001369996,0.0000807202,0.0004203167,0.00001084734,0.0006423009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005823871,"threshold_uncertainty_score":0.006979346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256637348788992,"score_gpt":0.2291349756921903,"score_spread":0.2165686022043004,"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."}}