{"id":"W2944543431","doi":"10.1002/cpe.5311","title":"Identification of Android malware using refined system calls","year":2019,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"European Commission; Intel Corporation; Silicon Valley Community Foundation","keywords":"Malware; System call; Opcode; Feature selection; Computer science; Android malware; Android (operating system); Data mining; Artificial intelligence; Machine learning; Computer security; Operating system","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":[0.0006425835,0.0006321089,0.0006162229,0.003481333,0.000270441,0.0009522216,0.0003347876,0.0003654191,0.0003746213],"category_scores_gemma":[0.004111989,0.0001330366,0.0007518812,0.0009025508,0.0003592638,0.0006496636,0.0005104958,0.0005006731,0.0002779085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003411286,"about_ca_system_score_gemma":0.0004974221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001748858,"about_ca_topic_score_gemma":0.001476217,"domain_scores_codex":[0.9986945,0.0001739518,0.0001103385,0.000163402,0.0007499821,0.0001078125],"domain_scores_gemma":[0.9967738,0.001273669,0.0005152374,0.0003325013,0.001011513,0.00009330393],"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":[0.000828734,0.000399867,0.1951993,0.0004011905,0.0002517294,0.001024921,0.0005131447,0.1399302,0.09819515,0.003614503,0.004505515,0.5551358],"study_design_scores_gemma":[0.00002176066,0.0002937471,0.08533588,0.00005103887,0.0001013507,0.0007176918,0.0001847787,0.8629802,0.04435355,0.002972074,0.002898216,0.00008964437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8425795,0.0004251057,0.1515475,0.0002177504,0.00004357821,0.0001636446,0.0007328357,0.002122557,0.002167504],"genre_scores_gemma":[0.9693303,0.00007416746,0.02948155,0.00002334411,0.00001695781,0.00004270133,0.0006765688,0.00001877079,0.000335722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003481333,"threshold_uncertainty_score":0.003477335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672593922934771,"score_gpt":0.3213727862077966,"score_spread":0.3046468469784489,"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."}}