{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-transfer-learning-for-static-malware","title":"Deep Transfer Learning for Static Malware Classification","arxiv_id":"1812.07606","date":"2018-12-18","proceeding":null,"authors":["Li Chen"],"abstract":"We propose to apply deep transfer learning from computer vision to static\nmalware classification. In the transfer learning scheme, we borrow knowledge\nfrom natural images or objects and apply to the target domain of static malware\ndetection. As a result, training time of deep neural networks is accelerated\nwhile high classification performance is still maintained. We demonstrate the\neffectiveness of our approach on three experiments and show that our proposed\nmethod outperforms other classical machine learning methods measured in\naccuracy, false positive rate, true positive rate and $F_1$ score (in binary\nclassification). We instrument an interpretation component to the algorithm and\nprovide interpretable explanations to enhance security practitioners' trust to\nthe model. We further discuss a convex combination scheme of transfer learning\nand training from scratch for enhanced malware detection, and provide insights\nof the algorithmic interpretation of vision-based malware classification\ntechniques.","url_abs":"http://arxiv.org/abs/1812.07606v1","url_pdf":"http://arxiv.org/pdf/1812.07606v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-transfer-learning-for-static-malware","repo_url":"https://github.com/mitchfwx/ISA480","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"malware-classification","task_name":"Malware Classification"},{"task_slug":"malware-detection","task_name":"Malware Detection"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}