[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118602-en":3,"doc-seo-118602-105":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},118602,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Evaluating Machine Learning Algorithms for Effective Network Protocol Classification","The study evaluates machine learning approaches for classifying network protocols under conditions that challenge traditional packet-inspection methods, including encrypted traffic and dynamic port assignment. Three models—Decision Tree, Random Forest, and Naive Bayes—are compared using precision, recall, and F1 score. Results show Random Forest and Decision Tree outperform Naive Bayes, with Random Forest achieving the highest accuracy at 96%. Findings support machine-learning-based protocol management and motivate further work on improving classification frameworks.","Evaluating Machine Learning Algorithms for Effective Network Protocol Classification  \nSalam Allawi Hussein  \nDepartment of Telecommunications Sze´chenyi Istva´n University Gyo¨r Gyr, Hungary University of Al-Qadisiyah [salam.allawi@sze.hu](salam.allawi@sze.hu)  \nSndor R. Rps  \nDepartment of Telecommunications Sze´chenyi Istva´n University Gyo¨r Gyr, Hungary [repas.sandor@sze.hu](repas.sandor@sze.hu)  \nAbstract—The current study illustrates the effectiveness of machine learning for the classification of protocols. Many critical operations on the network need to be observed, such as traffic analysis, quality of services, and traffic optimization. Given the emerging complexity of the network environment, it has become a challenge for a traditional classifier to deal with encrypted traffic and dynamic port assignment by data traffic. In the current study, three machine learning models were used and examined, named Decision Tree (TD), Random Forest (RF), and Naive Bayes (NB), which were evaluated based on metrics such as precision, precision, recall, and F1 score. The results indicated that both the Random Forest and the Decision tree outperform the NB, the highest achievement of the accuracy was for Random Forest with 96 % . This work shows the potential of using machine learning for the management of modern networks and provides the foundation for further studies geared towards optimizing protocol classification frameworks.  \nKeywords: Machine learning, classification, network traffic  \nI. INTRODUCTION  \nNetwork security, traffic analysis, and quality-of-service management are among some domains that benefit from the classification of network protocols [1] [2] . Accurate network protocol identification is a major enabler for promoting effective traffic management and mitigating security threats [3]  \n[4] . However, conventional methods of protocol classification generally rely on packet inspection and port-based techniques, which are increasingly showing diminished efficacy because of growing encrypted traffic use and dynamic port allocations. Machine learning is a suitable alternative since it can exploit statistical patterns within network data to classify protocols [5] . Unlike ordinary rule-based methods, machine learning algorithms could learn exceptionally complex and dynamic traffic patterns, matching well with modern networks’ intricacies [6] . Random Forest, Decision Tree, and Naive Bayes are among the algorithms evaluated for their ability to learn and efficiently classify network protocols.  \nSome researchers investigate the role of machine learning in network event and protocol classification, whilst Shafiq et al. [7] performed an elaborate classification study on network traffic using various machine learning methods. Using a collection of network traffic capture tools, the authors created a real-time internet dataset and extracted relevant features for analysis. They applied four different classifiers: support vector  \nmachine, C4.5 decision tree, and nave Bayes, and artificial neural network. The authors also showed that, with respect to accuracy, the C4.5 decision tree algorithm gave the best performance for classifying network traffic.  \nIn [8],Lotfollahi et al., introduce ”Deep Packet,” a deep learning framework for classifying network traffic, including encrypted data, without manual feature extraction. It combines stacked autoencoders (SAE) and convolutional neural networks (CNN) to analyze raw packet data. Deep Packet can categorize traffic into types like FTP and P2P and identify applications such as BitTorrent and Skype, distinguishing between VPN and non-VPN traffic as well. Evaluations on the UNB ISCX VPN-nonVPN dataset show a recall of 0.98 for application identification and 0.94 for traffic categorization, surpassing existing methods.  \nDespite significant advancements in network traffic classification, several challenges remain [2] . The rapid evolution of network protocols requires models to adapt co","cbCaibbm4lDGh5bC","https://ap.wps.com/l/cbCaibbm4lDGh5bC","pdf",344941,1,5,"English","en",105,"# Introduction\n## Background and motivation\n## Related work\n## Remaining challenges\n# Methodology\n## Machine learning models\n## Framework and datasets\n# Results and Discussion\n## Performance evaluation and metrics\n# Conclusion\n## Summary and future research directions","[{\"question\":\"Why are traditional protocol classification methods less effective?\",\"answer\":\"Encrypted traffic and dynamic port allocations reduce the reliability of packet inspection and port-based techniques. The study highlights these factors as key limitations.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"Decision Tree, Random Forest, and Naive Bayes are used to classify protocols. They are assessed on standard metrics including precision, recall, and F1 score.\"},{\"question\":\"What model achieved the highest accuracy and what was the value?\",\"answer\":\"Random Forest achieved the highest accuracy, reaching 96%. The study reports that Random Forest and Decision Tree outperform Naive Bayes.\"}]","Evaluating Machine Learning Algorithms for Effective Network Protocol Classification | PDF",1785684463,13,{"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},"evaluating-machine-learning-algorithms-for-effective-network-protocol-classification","",{"@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/evaluating-machine-learning-algorithms-for-effective-network-protocol-classification/118602/",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","2026-08-02",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},"Why are traditional protocol classification methods less effective?","Question",{"text":75,"@type":76},"Encrypted traffic and dynamic port allocations reduce the reliability of packet inspection and port-based techniques. The study highlights these factors as key limitations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are evaluated in the study?",{"text":80,"@type":76},"Decision Tree, Random Forest, and Naive Bayes are used to classify protocols. They are assessed on standard metrics including precision, recall, and F1 score.",{"name":82,"@type":73,"acceptedAnswer":83},"What model achieved the highest accuracy and what was the value?",{"text":84,"@type":76},"Random Forest achieved the highest accuracy, reaching 96%. The study reports that Random Forest and Decision Tree outperform Naive Bayes.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"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":21,"slug":137},19,"General","general"]