[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120086-en":3,"doc-seo-120086-105":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":20,"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},120086,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning-Driven Detection of Malicious URL - Comparative Analysis of Random Forest and SVMs - Research paper","The rapid growth of malicious websites creates a serious risk to users by deceiving them and enabling compromise through client-side attacks. Conventional defenses such as blacklisting struggle to keep pace with constantly evolving threats. This study proposes a machine learning workflow to automatically detect malicious URLs and evaluates the comparative effectiveness of Random Forest (RF) and Support Vector Machines (SVMs). It also examines how undersampling influences classification accuracy using a dataset of malicious and benign URL properties. Results show both models perform strongly, with RF slightly higher at 86.15% versus SVM at 85.38%.","JITE, 8 (1) July 2024 ISSN 2549-6247 (Print) ISSN2549-6255 (Online)  \nJITE (Journal of Informatics and Telecommunication Engineering)  \nAvailable online [http://ojs.uma.ac.id/index.php/jite](http://ojs.uma.ac.id/index.php/jite) DOI : 10.31289/jite.v8i1.11844  \n| Received: 13-May-2024 | Accepted: 26-July-2024 | Published: 31-July-2024 |\n| --- | --- | --- |\n\nMachine Learning-Driven Detection of Malicious URL: Comparative Analysis of Random ForestandSVMs  \nHikmahAdwin Adam1)*, Shaquil Fathza Nasution2), Rikky Rifaldo Simanungkalit3), Ikhsan  \nHafid Diansyah4)  \n1,2,3,4) Software Engineering Technology Study Program, Politeknik Negeri Medan  \nMedan, Indonesia  \n*Coresponding Email: [shaquilfathzanasution@students.polmed.ac.id](shaquilfathzanasution@students.polmed.ac.id)  \nAbstrak  \nDi dunia yang didorong oleh internet seperti saat ini, semakin banyak situs web berbahaya yang bermunculan dan menimbulkan ancaman signifikan. Situs web ini bertujuanuntuk menipu pengguna dan membahayakan data merekamelalui serangan di sisi klien. Metode tradisional seperti daftar hitam (blacklist) kesulitan untuk mengikuti perkembangan pesat dari ancaman tersebut. Penelitian ini mengatasi tantangan ini dengan mengusulkan pendekatan berbasis machine learning untuk deteksi otomatis URL berbahaya. Studi ini bertujuan untuk membandingkan efektivitas dari dua algoritma machine learning yang populer, Random Forest (RF) dan Support Vector Machines (SVM), dalam mendeteksi URL berbahaya. Selanjutnya, kami menyelidiki dampak undersampling pada kinerja klasifikasi. Set data (dataset) yang berisi properti URL dari situs web berbahaya (malicious) dan tidak berbahaya (benign) digunakan untuk melatih model RF dan SVM. Model dievaluasi berdasarkan akurasi merekadalam mengklasifikasikan URL. Baik model RF maupun SVM mencapai hasil yang menjanjikan, dengan model RF mengungguli SVM dengan akurasi 86,15% dibandingkan dengan SVM 85,38% . Namun, potensi ketidakseimbangan kelas dalam set data (dataset) mengharuskan eksplorasi lebih lanjut metode alternatif lainnya seperti oversampling untuk meningkatkan kinerja pada kedua kategori situs web. Penelitian ini menunjukkan kelayakan machine learning untuk deteksi URL berbahaya secara akurat. RF muncul sebagai algoritma yang sedikit lebih efektif dibandingkandengan SVM dalam konteks khusus ini. Studi ini berkontribusi pada pengembangan tindakan keamanan situs web yang lebih kuat dengan menyoroti potensi machine learning untuk deteksi URL berbahaya secara otomatis. Penelitian selanjutnya dapat mengeksplorasi aplikasi teknik oversampling dan menyelidiki algoritma machine learning tambahan untuk lebih meningkatkan akurasi deteksi.  \nKata Kunci: URL, Malicious, Benign, Random Forest, Support Vector Machines, Undersampling  \nAbstract  \nThe proliferation of malicious websites poses a significant threat in today's internet-driven world. These websites aim to deceive users and compromise their data through client-side attacks. Traditional methods like blacklisting struggle to keep pace with the evolving nature of such threats. This research addresses this challenge by proposing a machine learning-based approach for the automatic detection of malicious URLs. This study aims to compare the effectiveness of two popular machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVMs), in detecting malicious URLs. We investigate the impact of undersampling on classification performance. A dataset containing URL properties of both malicious and benign websites was used to train the RFandSVM models. The models were evaluated based on their accuracy in classifying URLs. Both RF and SVM models achieved promising results, with the RF model outperforming SVM with an accuracy of 86.15% compared toSVM's 85.38%. However, the potential for class imbalance within the dataset necessitates further exploration of alternative methods like oversampling for potentially improved performance across both website categories. This research demons","cbCaiqJP8Cz1YmVI","https://ap.wps.com/l/cbCaiqJP8Cz1YmVI","pdf",680419,1,10,"English","en",105,"# Introduction\n## Background and motivation\n## Related work and gaps\n# Methodology\n## Dataset and features\n## Models: Random Forest and SVMs\n## Undersampling experiment\n# Results and discussion\n## Accuracy comparison\n## Impact of class imbalance\n# Conclusion and future work","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the challenge of accurately detecting malicious URLs as traditional defenses like blacklists become less effective against evolving threats.\"},{\"question\":\"Which machine learning algorithms are compared?\",\"answer\":\"The research compares Random Forest (RF) and Support Vector Machines (SVMs) for detecting malicious URLs.\"},{\"question\":\"How does undersampling affect the classification performance?\",\"answer\":\"The study investigates the impact of undersampling on classification performance, noting that class imbalance makes further exploration—such as oversampling—necessary for potentially improved results.\"}]","Machine Learning-Driven Detection of Malicious URL - Comparative Analysis of Random Forest and SVMs - Research paper | PDF",1785728081,25,{"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-driven-detection-of-malicious-url-comparative-analysis-of-random-forest-and-svms-research-paper","",{"@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-driven-detection-of-malicious-url-comparative-analysis-of-random-forest-and-svms-research-paper/120086/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study addresses the challenge of accurately detecting malicious URLs as traditional defenses like blacklists become less effective against evolving threats.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared?",{"text":80,"@type":76},"The research compares Random Forest (RF) and Support Vector Machines (SVMs) for detecting malicious URLs.",{"name":82,"@type":73,"acceptedAnswer":83},"How does undersampling affect the classification performance?",{"text":84,"@type":76},"The study investigates the impact of undersampling on classification performance, noting that class imbalance makes further exploration—such as oversampling—necessary for potentially improved results.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]