{"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/an-efficient-approach-for-malware-detection","title":"An Efficient Approach For Malware Detection Using PE Header Specification","arxiv_id":null,"date":"2020-06-11","proceeding":null,"authors":["Tina Rezaei","Ali Hamzeh"],"abstract":"Following  the dramatic  growth  of  malware  and the  essential  role  of  computer  systems  in  our  daily  lives,  the security  of  computer  systems  and  the  existence  of  malware detection   systems   become   critical.   In   recent   years,   many machine   learning   methods   have   been   used   to   learn   the behavioral  or  structural  patterns  of  malware.  Because  of  their high generalization capability, they have achieved great success in   detecting   malware.   In   this   paper,   to   identify   malware programs,  features  extracted  based  on  the  header  and  PE  file structure  are  used  to  train  several  machine  learning  models. The   proposed   method   identifies   malware   programs   with 95.59%  accuracy  using  only  nine  features,  the  values  of  which have a significant difference between malware and benign files. Due   to   the   high   speed   of   the   proposed   model   in   feature extraction and the low number of extracted features, which lead to  faster  model  training,  the  proposed  method  can  be  used  in real-time malware detection systems","url_abs":"https://www.sid.ir/FileServer/SE/648E20200653.pdf","url_pdf":"https://www.sid.ir/FileServer/SE/648E20200653.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":"an-efficient-approach-for-malware-detection","repo_url":"https://github.com/Tina-Rezaei/malware-detection-based-on-pe-header","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"malware-detection","task_name":"Malware Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}