{"url":"/sota/malware-classification-on-malimg-dataset","task":{"name":"Malware Classification","url":"/task/malware-classification","note":null},"dataset":{"name":"Malimg Dataset","url":"/dataset/malimg"},"category":"Miscellaneous","categories":["Miscellaneous"],"category_note":null,"description":"**Malware Classification** is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted. A static signature can be based on a byte-code sequence, binary assembly instruction, or an imported Dynamic Link Library (DLL). Dynamic signatures can be based on file system activities, terminal commands, network communications, or function and system call sequences.\r\n\r\n\r\n<span class=\"description-source\">Source: [Behavioral Malware Classification using Convolutional Recurrent Neural Networks ](https://arxiv.org/abs/1811.07842)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy (10-fold)","Macro F1 (10-fold)","Accuracy","Macro F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy (10-fold)":"higher","Macro F1 (10-fold)":"higher","Accuracy":"higher","Macro F1":"higher"}},"counts":{"rows":5,"rows_with_code":4,"rows_with_paper_page":5,"rows_dated":5,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Gray-scale IMG CNN","metrics":{"Accuracy (10-fold)":"0.9848","Macro F1 (10-fold)":"0.9580"},"uses_additional_data":false,"paper_date":"2018-08-27","paper":"/paper/using-convolutional-neural-networks-for-1","paper_url":"https://link.springer.com/article/10.1007/s11416-018-0323-0","paper_title":"Using Convolutional Neural Networks for Classification of Malware represented as Images","code":"https://github.com/danielgibert/mlw_classification_cnn_img","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"GA Designed Deep CNN","metrics":{"Accuracy":"0.985","Macro F1":"0.9391"},"uses_additional_data":false,"paper_date":"2022-07-18","paper":"/paper/designing-deep-convolutional-neural-networks-1","paper_url":"https://ieeexplore.ieee.org/document/9870218","paper_title":"Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"GRU + SVM","metrics":{"Accuracy":"0.8492"},"uses_additional_data":false,"paper_date":"2017-12-31","paper":"/paper/towards-building-an-intelligent-anti-malware","paper_url":"http://arxiv.org/abs/1801.00318v2","paper_title":"Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification","code":"https://github.com/AFAgarap/malware-classification","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"FFNN + SVM","metrics":{"Accuracy":"0.8047"},"uses_additional_data":false,"paper_date":"2017-12-31","paper":"/paper/towards-building-an-intelligent-anti-malware","paper_url":"http://arxiv.org/abs/1801.00318v2","paper_title":"Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification","code":"https://github.com/AFAgarap/malware-classification","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"CNN + SVM","metrics":{"Accuracy":"0.7723"},"uses_additional_data":false,"paper_date":"2017-12-31","paper":"/paper/towards-building-an-intelligent-anti-malware","paper_url":"http://arxiv.org/abs/1801.00318v2","paper_title":"Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification","code":"https://github.com/AFAgarap/malware-classification","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}