[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125835-en":3,"doc-seo-125835-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125835,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning for Malware Detection in Network Traffic","Developing efficient malware detection systems is increasingly critical as cybersecurity threats continue to expand and evolve. This research addresses the challenge of identifying malware and safeguarding digital assets against cyber-attacks where conventional techniques struggle to adapt. A machine-learning model is proposed for malware detection in network traffic, evaluated using an assessment matrix with Accuracy, Precision, Recall, and F1 Score. Results show Adaboost achieves the strongest overall performance, with TPR above 97% and FPR under 4%, supporting practical organizational defense and operational decision-making.","Machine Learning for Malware Detection in Network Traffic  \nAyorinde Henry Omopintemi  \nUniversity of Bradford Bradford, United Kingdom [a.h.omopintemi@bradfor.ac.uk](a.h.omopintemi@bradfor.ac.uk)  \nIbrahim Ghafir  \nUniversity of Bradford Bradford, United Kingdom [i.ghafir@bradford.ac.uk](i.ghafir@bradford.ac.uk)  \nShadi Eltanani Oxford Brookes University Oxford, United Kingdom[seltanani@brookes.ac.uk](seltanani@brookes.ac.uk)  \nSohag Kabir  \nUniversity of Bradford Bradford, United Kingdom[s.kabir2@bradford.ac.uk](s.kabir2@bradford.ac.uk)  \nMoemedi Lefoane  \nUniversity of Bradford Bradford, United Kingdom [m.lefoane@bradford.ac.uk](m.lefoane@bradford.ac.uk)  \nABSTRACT  \nDeveloping advanced and efficient malware detection systems is becoming significant in light of the growing threat landscape in cybersecurity. This work aims to tackle the enduring problem ofidentifying malware and protecting digital assets from cyber-attacks. Conventional methods frequently prove ineffective in adjusting to the ever-evolving field of harmful activity. As such, novel approaches that improve precision while simultaneously taking into account the ever-changing landscape of modern cybersecurity problems are needed. To address this problem this research focuses on the detection of malware in network traffic. This work proposesa machine-learning-based approach for malware detection, with particular attention to the Random Forest (RF), Support Vector Machine (SVM), and Adaboost algorithms. In this paper, the model’s performance was evaluated using an assessment matrix. Included the Accuracy (AC) for overall performance, Precision (PC) for positive predicted values, Recall Score (RS) for genuine positives, and the F1 Score (SC) for a balanced viewpoint. A performance comparison has been performed and the results reveal that the built model utilizing Adaboost has the best performance. The TPR for the three classifiers performs over 97% and the FPR performs \u003C 4% for each of the classifiers. The created model in this paper has the potential to help organizations or experts anticipate and handle malware. The proposed model can be used to make forecasts and provide management solutions in the network’s everyday operational activities.  \nCCS CONCEPTS  \n• Security and privacy → Intrusion detection systems.  \nKEYWORDS  \nMachine learning, Malware Detection, Intrusion Detection, Malware Analysis, The Adaboost Algorithm, Random Forest, K-Nearest Neighbor Algorithm  \nThis work is licensed under a Creative Commons Attribution International 4.0 License.  \nICFNDS’23, December 21–22, 2023, Dubai, United Arab Emirates © 2023 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-0903-6/23/12  \n[https://doi.org/10.1145/3644713.3644804](https://doi.org/10.1145/3644713.3644804)  \nACM Reference Format:  \nAyorinde Henry Omopintemi, Ibrahim Ghafir, Shadi Eltanani, Sohag Kabir, and Moemedi Lefoane. 2023. Machine Learning for Malware Detection in Network Traffic. In The International Conference on Future Networksand Distributed Systems (ICFNDS’23), December 21–22, 2023, Dubai, United Arab Emirates. ACM, New York, NY, USA, 6 pages. [https://doi.org/10.1145/](https://doi.org/10.1145/)[ ](https://doi.org/10.1145/)3644713.3644804  \n1 INTRODUCTION  \nNumerous electronic equipment has bad experiences alteration by malware in the digital age. Malicious software that is created with the intention of harming a victim is where the name malware originates. Malware can infiltrate networks, infect computers and other smart devices, steal sensitive data, damage vital infrastructure, and more [22] . These programmes include malware such as ransomware, rootkits, worms, spyware, bots, and viruses. According to [21] IT services claims that in only one year, one billion emails were exposed, impacting one in five internet users, and resulting in data breaches that cost organisations, on average, $4.35 million in 2022 . The first half of 2022, there were about 236 . 1 million ransomware ass","cbCairshrw9aPEJD","https://ap.wps.com/l/cbCairshrw9aPEJD","pdf",429686,1,6,"English","en",105,"# Abstract\n# Keywords\n# 1 Introduction","[{\"question\":\"What is the main goal of this research?\",\"answer\":\"To detect malware in network traffic and protect digital assets from cyber-attacks by using machine learning methods that adapt to evolving threats.\"},{\"question\":\"Which machine-learning algorithms are evaluated in the study?\",\"answer\":\"Random Forest (RF), Support Vector Machine (SVM), and Adaboost are considered for the proposed malware detection approach.\"},{\"question\":\"How is model performance measured and what is the key result?\",\"answer\":\"Performance is evaluated with an assessment matrix using Accuracy, Precision, Recall (RS), and F1 Score. The results show that the model using Adaboost achieves the best performance, with TPR over 97% and FPR below 4% for each classifier.\"}]","Machine Learning for Malware Detection in Network Traffic | PDF",1785901486,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-malware-detection-in-network-traffic","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-malware-detection-in-network-traffic/125835/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this research?","Question",{"text":75,"@type":76},"To detect malware in network traffic and protect digital assets from cyber-attacks by using machine learning methods that adapt to evolving threats.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning algorithms are evaluated in the study?",{"text":80,"@type":76},"Random Forest (RF), Support Vector Machine (SVM), and Adaboost are considered for the proposed malware detection approach.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance measured and what is the key result?",{"text":84,"@type":76},"Performance is evaluated with an assessment matrix using Accuracy, Precision, Recall (RS), and F1 Score. The results show that the model using Adaboost achieves the best performance, with TPR over 97% and FPR below 4% for each classifier.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]