{"id":"W2996149581","doi":"10.48550/arxiv.1912.09303","title":"SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Robustness (evolution); Adversarial system; Machine learning; Artificial intelligence; Intrusion detection system; Generative grammar; Adversarial machine learning; Metaheuristic; Attack surface; Attack model; Computer security","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":[0.002378351,0.001483983,0.001096636,0.000813348,0.0003056906,0.0009043058,0.001533496,0.001358137,0.001148541],"category_scores_gemma":[0.004885441,0.0004126091,0.001129679,0.0004072547,0.001097386,0.001283348,0.001228895,0.002003703,0.0002982304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019025,"about_ca_system_score_gemma":0.000968973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002501223,"about_ca_topic_score_gemma":0.00172376,"domain_scores_codex":[0.9990019,0.0004177209,0.00005323331,0.0001560666,0.0002337912,0.0001373296],"domain_scores_gemma":[0.9973266,0.001526988,0.0002529723,0.0003901518,0.0003932751,0.0001099158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007248648,0.00007832308,0.000945095,0.0000506895,0.00006221444,0.00004390343,0.0000357951,0.9591845,0.002351684,0.00397895,0.001000229,0.03219618],"study_design_scores_gemma":[0.000006987819,0.00003241338,0.000069523,0.00000406927,0.000006026692,0.00001448457,0.000003853457,0.997801,0.0008791566,0.0009054862,0.0002733741,0.000003528336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1215345,0.0009998274,0.8670501,0.0007047745,0.0002609736,0.000177665,0.0001086239,0.003103986,0.006059548],"genre_scores_gemma":[0.8408595,0.0002348673,0.1558899,0.0004655916,0.00006369661,0.0001124304,0.000181053,0.0001607293,0.002032186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002501223,"threshold_uncertainty_score":0.01257801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04420461988631143,"score_gpt":0.1853549323461231,"score_spread":0.1411503124598116,"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."}}