{"id":1857,"date":"2024-02-15T16:34:00","date_gmt":"2024-02-15T07:34:00","guid":{"rendered":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/?p=1857"},"modified":"2025-11-28T12:53:36","modified_gmt":"2025-11-28T03:53:36","slug":"improving-malware-detection-accuracy-through-hyperparameter-tuning-with-parallel-processing-undergraduate-students-research-for-the-2023-academic-year","status":"publish","type":"post","link":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/en\/archives\/1857","title":{"rendered":"&#8220;Improving malware detection accuracy through hyperparameter tuning with parallel processing &#8220;(Undergraduate student\u2019s research for the 2023 academic year)"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">In this study, we improved the efficiency of model development and enhanced malware detection accuracy by performing parallel hyperparameter tuning for machine learning models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Traditional antivirus detection methods primarily rely on pattern matching, in which files are compared against previously known malicious code. However, this approach faces limitations in responding to the large number of new malware variants that appear daily. To address this issue, we constructed a malware classification model using a public dataset containing information on more than 50,000 files, including malicious samples. Furthermore, by using <a href=\"https:\/\/optuna.readthedocs.io\/\" data-type=\"link\" data-id=\"https:\/\/optuna.readthedocs.io\/\">Optuna<\/a>\u2014a library that automatically searches for optimal hyperparameters\u2014we achieved efficient optimization through parallel processing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As a result, our model achieved an accuracy of 99.35%, surpassing the 98.00% reported in previous studies. Validation on an additional dataset also demonstrated high accuracy in the 97%\u201399% range, confirming that this approach provides a robust and generalizable methodology capable of handling unknown malware.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In summary, our findings indicate that efficient hyperparameter optimization using parallel processing contributes significantly to improving the accuracy of malware detection.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"890\" height=\"734\" src=\"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wordpress\/wp-content\/uploads\/2025\/11\/1121\u914d\u4fe1\u5206_\u30b3\u30f3\u30c6\u30f3\u30c4A_\u753b\u50cf-1.jpg\" alt=\"\" class=\"wp-image-1855\" style=\"width:640px\" srcset=\"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wordpress\/wp-content\/uploads\/2025\/11\/1121\u914d\u4fe1\u5206_\u30b3\u30f3\u30c6\u30f3\u30c4A_\u753b\u50cf-1.jpg 890w, https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wordpress\/wp-content\/uploads\/2025\/11\/1121\u914d\u4fe1\u5206_\u30b3\u30f3\u30c6\u30f3\u30c4A_\u753b\u50cf-1-300x247.jpg 300w, https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wordpress\/wp-content\/uploads\/2025\/11\/1121\u914d\u4fe1\u5206_\u30b3\u30f3\u30c6\u30f3\u30c4A_\u753b\u50cf-1-768x633.jpg 768w\" sizes=\"auto, (max-width: 890px) 100vw, 890px\" \/><figcaption class=\"wp-element-caption\">Flowchart of the implemented system<\/figcaption><\/figure>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>we improved the efficiency of model development and enhanced malware detection accuracy by performing parallel hyperparameter tuning for machine learning models.<\/p>\n","protected":false},"author":1,"featured_media":1854,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_lmt_disableupdate":"no","_lmt_disable":"","_locale":"en_US","_original_post":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/?p=1853","footnotes":""},"categories":[24],"tags":[],"class_list":["post-1857","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-24","en-US"],"modified_by":"\u89d2\u7530\u3000\u7a1a\u5b99","_links":{"self":[{"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/posts\/1857","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/comments?post=1857"}],"version-history":[{"count":2,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/posts\/1857\/revisions"}],"predecessor-version":[{"id":1859,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/posts\/1857\/revisions\/1859"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/media\/1854"}],"wp:attachment":[{"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/media?parent=1857"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/categories?post=1857"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/tags?post=1857"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}