[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117147-en":3,"doc-seo-117147-105":30,"detail-sidebar-cat-0-en-105":94},{"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":21,"is_downloadable":21,"audit_status":21,"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},117147,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Detection of DDoS Attacks Using Machine Learning","Rapid growth in information technology has made network security increasingly critical, particularly with the rise of Distributed Denial of Service (DDoS) attacks. With Cisco predicting a significant increase in global DDoS activity, this research develops a Machine Learning detection model using the Decision Tree algorithm. The approach leverages the APA_DDoS dataset to recognize DDoS attack patterns and trigger Telegram-based notifications for faster response. Evaluation reports perfect performance, with accuracy, precision, recall, and F1 score all reaching 100%.","DETECTION OF DDoS ATTACKS USING MACHINE  \nLEARNING  \nName  \nStudent Id Number Supervisor  \n: M. Hasrul  \n: 6404201011  \n: Jaroji, M.Kom  \nABSTRACT  \nIn the rapid development of information technology, network security has become a crucial issue with the increasing number of users and threats, especially Distributed Denial of Service (DDoS) attacks. According to Cisco's predictions, global DDoS attacks are expected to increase by 15.4 million in 2023. This research proposes the development of a Machine Learning model using the Decision Tree algorithm, proven to quickly and accurately detect DDoS attacks. The model is constructed using the APA_DDoS dataset, aiming to build an efficient and effective system for recognizing DDoS attack patterns based on Machine Learning and providing notifications via Telegram. The primary goal is to establish a system that efficiently and effectively identifies DDoS attack patterns using Machine Learning with the Decision Tree algorithm, benefiting both individuals and industries in detecting DDoS attacks. The research results in an efficient and effective detection system for recognizing DDoS attack patterns using Machine Learning with the Decision Tree algorithm, capable of accurately identifying DDoS attack patterns and providing a swift response by sending Telegram notifications. The model evaluation shows perfect results with accuracy, precision, recall, and F1 score reaching 100%, indicating that the model can predict every class very well.  \nKeywords: DDoS, Machine Learning, Decision Tree","cbCaimwiZ8ebJo5E","https://ap.wps.com/l/cbCaimwiZ8ebJo5E","pdf",13178,2,1,"English","en",105,"# Abstract\n# Keywords\n## DDoS context and challenge\n## Proposed Decision Tree model\n## Dataset and notification mechanism\n## Evaluation results","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses the detection of Distributed Denial of Service (DDoS) attacks as network threats increase.\"},{\"question\":\"Which Machine Learning approach and dataset are used?\",\"answer\":\"The system uses a Decision Tree algorithm and the APA_DDoS dataset to learn and recognize DDoS patterns.\"},{\"question\":\"How does the system respond when an attack is detected?\",\"answer\":\"It sends Telegram notifications to enable a quick response to detected DDoS activity.\"},{\"question\":\"How accurate is the proposed detection model?\",\"answer\":\"The evaluation shows perfect results, with accuracy, precision, recall, and F1 score all reaching 100%.\"}]","Detection of DDoS Attacks Using Machine Learning | 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