{"url":"/sota/malware-detection-on-malnet","task":{"name":"Malware Detection","url":"/task/malware-detection","note":null},"dataset":{"name":"MalNet","url":"/dataset/malnet"},"category":"Miscellaneous","categories":["Miscellaneous"],"category_note":null,"description":"**Malware Detection** is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as [malicious malware redirects on WordPress site](https://secure.wphackedhelp.com/blog/wordpress-malware-redirect-hack-cleanup/) (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware\r\n\r\n\r\n<span class=\"description-source\">Source: [The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey ](https://arxiv.org/abs/1911.02621)</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":["F1 score"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1 score":"higher"}},"counts":{"rows":3,"rows_with_code":3,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SHERLOCK (family)","metrics":{"F1 score":"0.878"},"uses_additional_data":false,"paper_date":"2022-08-15","paper":"/paper/self-supervised-vision-transformers-for","paper_url":"https://arxiv.org/abs/2208.07049v1","paper_title":"Self-Supervised Vision Transformers for Malware Detection","code":"https://github.com/sachith500/sherlock","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":4,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"SHERLOCK (type)","metrics":{"F1 score":"0.876"},"uses_additional_data":false,"paper_date":"2022-08-15","paper":"/paper/self-supervised-vision-transformers-for","paper_url":"https://arxiv.org/abs/2208.07049v1","paper_title":"Self-Supervised Vision Transformers for Malware Detection","code":"https://github.com/sachith500/sherlock","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":4,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"SHERLOCK","metrics":{"F1 score":"0.854"},"uses_additional_data":false,"paper_date":"2022-08-15","paper":"/paper/self-supervised-vision-transformers-for","paper_url":"https://arxiv.org/abs/2208.07049v1","paper_title":"Self-Supervised Vision Transformers for Malware Detection","code":"https://github.com/sachith500/sherlock","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":4,"n_samples":8,"n_pointer_only_licence":0}}],"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":3,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":4,"n_unverified":4,"n_samples":8,"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":12,"n_unverified":12,"n_samples":24,"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"}}}