[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120735-en":3,"doc-seo-120735-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120735,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Classifying Action of Internet Firewall Using Machine Learning Models","This research paper investigates the classification of internet traffic using various machine learning models, focusing on enhancing network security against cyber threats. The study employs six distinct classification models: K-Nearest Neighbor, SGD, Decision Tree, Random Forest, XGBoost, and Support Vector Machine. The dataset, derived from a university's firewall internet traffic records, comprises 65,532 instances and 11 features, with the 'Action' feature serving as the class label. Experimental results indicate that the XGBoost model demonstrates superior performance, achieving the highest accuracy, recall, precision, and F1-score, while the Decision Tree model exhibits the lowest time complexity. The paper concludes that both Random Forest and XGBoost models outperform others, with XGBoost notably achieving an accuracy of 99.9450%. KNN is highlighted for its runtime efficiency. Future work suggests utilizing larger datasets from diverse firewalls to further advance classification performance.","Classifying Action of Internet Firewall Using Machine Learning Models  \nGeetanjali Kakda, Dr. Eman Abdelfattah  \nSchool of Computer Science & Engineering  \nSacred Heart University, Fairfield, CT  \nAbstract  \nA firewall is a type of security mostly located at the entry and exit points of a network. This is necessary to improve the security of the network and defend against cyber threats. This study aims to classify the internet traffic using different Machine Learning classification models into four categories based on 11 features. The models applied are – K-Nearest Neighbor, SGD, Decision Tree, Random Forest , XGBoost, Support Vector Machine. The XGBoost model achieved the highest accuracy, recall, precision, F1score. Decision Tree has the least time complexity.  \nDataset Description  \n• This data set was collected from the internet traffic records on a University’s firewall.  \n• This is a multi-class data set.  \n• It consists of 65532 instances.  \n• There are 12 features in total.  \n• Action feature is used as a class.  \nExperimental Results & Analysis:  \nTABLE SHOWS VALUES OF ACCURACY, RECALL, PRECISION, F1SCORE AND TC  \nBAR CHART FOR ACCURACY KNN, SGD, DT , RF , XGBOOST and SVM MODELS  \nConclusion:  \nIn conclusion the study shows that the both Random Forest and XGBoost Models perform better than other models. XGBoost performs has the best performance in terms of accuracy of 99 .9450 % . KNN performs best in terms of runtime. In future work larger data can be handled extracted from different firewalls to achieve high performance of classification.  \nReferences:  \n[1] R. F. Naryanto and M. K. Delimayanti, \"Machine Learning Technique for Classification of Internet Firewall Data Using RapidMiner, \" 2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), Medan, Indonesia, 2022, pp. 155-159,doi: 10.1109/ELTICOM57747.2022.10037798.C  \nPublished by DigitalCommons@SHU, 2023 1","cbCaiuOH3vVelOtu","https://ap.wps.com/l/cbCaiuOH3vVelOtu","pdf",783917,1,"English","en",105,"# Abstract\n# Dataset Description\n# Experimental Results & Analysis\n# Conclusion","[{\"question\":\"What is the primary objective of this study?\",\"answer\":\"The primary objective is to classify internet traffic using different machine learning models to enhance network security and defend against cyber threats.\"},{\"question\":\"Which machine learning models were used in the study?\",\"answer\":\"The study applied K-Nearest Neighbor, SGD, Decision Tree, Random Forest, XGBoost, and Support Vector Machine models.\"},{\"question\":\"Which model achieved the highest accuracy, and what was its performance?\",\"answer\":\"The XGBoost model achieved the highest accuracy, with a performance of 99.9450%.\"}]","Classifying Action of Internet Firewall Using Machine Learning Models | 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is the primary objective of this study?","Question",{"text":73,"@type":74},"The primary objective is to classify internet traffic using different machine learning models to enhance network security and defend against cyber threats.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which machine learning models were used in the study?",{"text":78,"@type":74},"The study applied K-Nearest Neighbor, SGD, Decision Tree, Random Forest, XGBoost, and Support Vector Machine models.",{"name":80,"@type":71,"acceptedAnswer":81},"Which model achieved the highest accuracy, and what was its performance?",{"text":82,"@type":74},"The XGBoost model achieved the highest accuracy, with a performance of 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