[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-detail-450217-en":59,"doc-seo-450217-105":81},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":5,"data":60},{"doc_id":61,"user_id":62,"nickname":63,"user_avatar":64,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":66,"doc_content":67,"file_id":68,"file_url":69,"file_type":70,"file_size":71,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":72,"language":73,"language_code":74,"site_id":75,"html_lang":74,"table_of_contents":76,"faqs":77,"seo_title":78,"seo_description":66,"update_tm":79,"read_time":80},450217,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Mitigating distributed denial of service attacks using attribute subset selection with temporal convolutional networks","Distributed Denial of Service (DDoS) attacks continuously evolve and present inconsistent traffic patterns, making real-time identification and mitigation difficult. This study addresses the threat to digital and cyber infrastructures by proposing an intelligent attack-detection framework that applies min-max normalization for data preprocessing, Salp swarm-based attribute subset feature selection, and a temporal convolutional network for classification. Experiments on CIC-IDS-2017 and Edge-IIoT datasets validate improved accuracy of 99.56% and 99.65% over existing approaches.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMitigating distributed denial of service attacks using attribute subset selection with temporal convolutional networks  \nHayamAlamro1, Asmaa Mansour Alghamdi2, Asma Alshuhail3, ShoukiA. Ebad4􀀍, Mukhtar Ghaleb5, Hassan Alkhiri6, Hany Mahgoub7 & Malak Zayed Alamri8  \nNowadays, Distributed Denial of Service (DDoS) attacks have proved to be uncontrolled and arrive indifferent patterns and shapes. Therefore, it is hard to identify and resolve the preceding solutions. ADDoS attack is a mischievous try to interrupt the usual traffic of a target server, network, or service by overcoming the aim or its nearby framework with an overflow of Internet traffic. Then, numerous classification methods are applied in many research and are targeted to identify and resolve the attack of DDoS. DDoS attacks are implemented simply by utilizing the network’s flaws and by making desires for software services. The real-time detection of attacks is challenging to identify and alleviate. However, this solution is valuable as these attacks may lead to significant problems. Several deep learning (DL) methods are advanced to locate and analyze DDoS attacks. This study proposes a novel Intelligent Framework for Attack Detection Using Salp Swarm-Based Feature Selection and Deep Learning Architecture (IFAD-SSFSDLA) model. This paper aims to deliver a real-time DDoS attack detection system utilizing advanced optimization algorithms. As a primary step, the IFAD-SSFSDLA technique utilizes min-max normalization for the data pre-processing to transform, clean, and organize raw data into the structured pattern. In addition, the Salp swarm algorithm (SSA) is employed in the feature selection process to detect and maintain the most significant features to improve the model performance. The IFAD-SSFSDLA model implements the temporal convolutional network (TCN) method for the attack classification. To exhibit the enhanced performance ofthe IFAD-SSFSDLA model, a comprehensive experimental analysis is conducted using CIC-IDS-2017 and Edge-IIoT datasets. The performance validation ofthe IFAD-SSFSDLA model portrayed superior accuracy values of 99.56% and 99.65% over existing techniques under dual datasets.  \nKeywords DDoS attack detection, Deep learning, Data pre-processing, Salp swarm algorithm, Temporal convolutional network  \nDDoS attacks are among the most significant hazards to digital, network, and cyber structures1. Such attacks can cause considerable disruptions in some information communication technology (ICT) frameworks. There are several motives to launch DDoS attacks. This includes political and financial gains and interruption. DDoS attacks may disable services and networks by overpowering connection devices like routers, switches, relay links, and servers with illegal traffic2. It will pose both complete denial and degradation of service, which will lead to massive loss. The growing need for data centres and the Internet has worsened these problems. The increasing reliance on the dangerous infrastructure of the nation in ICT has affected the necessity for effective solutions  \n1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia. 2Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia. 3Department of Information Systems, College of Computer Sciences & Information Technology, King Faisal University, Al Hofuf, Saudi Arabia.  \n4Center for Scientific Research and Entrepreneurship, Northern Border University, Arar 73213, Saudi Arabia.  \n5College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia. 6Department of Computer Science, Faculty of Computing and Information Technology, Al- Baha University, Al- Baha, Saudi Arabia. 7Department of Computer Science Applied College at Maha","cbCaiupSOIdSQfmN","https://ap.wps.com/l/cbCaiupSOIdSQfmN","pdf",6018775,26,"English","en",105,"# Introduction\n# Proposed Framework (IFAD-SSFSDLA)\n## Data Pre-processing\n## Salp Swarm-Based Feature Selection\n## Temporal Convolutional Network Classification\n# Experimental Evaluation","[{\"question\":\"What challenge does the study target in DDoS defense?\",\"answer\":\"It targets the difficulty of real-time detection and mitigation caused by increasingly severe, sophisticated, and frequent DDoS traffic patterns.\"},{\"question\":\"How does the proposed IFAD-SSFSDLA model prepare the data?\",\"answer\":\"It uses min-max normalization to transform, clean, and organize raw data into a structured pattern for learning.\"},{\"question\":\"Which methods are used for feature selection and classification?\",\"answer\":\"Salp swarm algorithm (SSA) performs attribute subset feature selection, and a temporal convolutional network (TCN) is used for attack classification.\"}]","Mitigating distributed denial of service attacks using attribute subset selection with temporal convolutional networks | PDF",1790732489,66,{"code":4,"msg":82,"data":83},"ok",{"site_id":75,"language":74,"slug":84,"title":65,"keywords":85,"description":66,"schema_data":86,"social_meta":140,"head_meta":142,"extra_data":144,"updated_unix":145},"mitigating-distributed-denial-of-service-attacks-using-attribute-subset-selection-with-temporal-convolutional-networks","",{"@graph":87,"@context":139},[88,102,122],{"@type":89,"itemListElement":90},"BreadcrumbList",[91,95,97,100],{"item":92,"name":93,"@type":94,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":96,"name":9,"@type":94,"position":14},"https://docshare.wps.com/document/",{"item":98,"name":40,"@type":94,"position":99},"https://docshare.wps.com/document/research-report/",3,{"item":101,"name":65,"@type":94,"position":19},"https://docshare.wps.com/document/mitigating-distributed-denial-of-service-attacks-using-attribute-subset-selection-with-temporal-convolutional-networks/450217/",{"url":101,"name":65,"@type":103,"image":104,"author":109,"headline":65,"publisher":111,"fileFormat":114,"inLanguage":74,"description":66,"dateModified":115,"datePublished":116,"encodingFormat":114,"isAccessibleForFree":117,"interactionStatistic":118},"DigitalDocument",{"url":105,"@type":106,"width":107,"height":108},"https://docshare.wps.com/thumbnails/mitigating-distributed-denial-of-service-attacks-using-attribute-subset-selection-with-temporal-convolutional-networks/450217.png","ImageObject",300,407,{"name":63,"@type":110},"Person",{"url":92,"name":112,"@type":113},"DocShare","Organization","application/pdf","2026-10-06","2026-09-30",true,{"@type":119,"interactionType":120,"userInteractionCount":24},"InteractionCounter",{"@type":121},"ViewAction",{"@type":123,"mainEntity":124},"FAQPage",[125,131,135],{"name":126,"@type":127,"acceptedAnswer":128},"What challenge does the study target in DDoS defense?","Question",{"text":129,"@type":130},"It targets the difficulty of real-time detection and mitigation caused by increasingly severe, sophisticated, and frequent DDoS traffic patterns.","Answer",{"name":132,"@type":127,"acceptedAnswer":133},"How does the proposed IFAD-SSFSDLA model prepare the data?",{"text":134,"@type":130},"It uses min-max normalization to transform, clean, and organize raw data into a structured pattern for learning.",{"name":136,"@type":127,"acceptedAnswer":137},"Which methods are used for feature selection and classification?",{"text":138,"@type":130},"Salp swarm algorithm (SSA) performs attribute subset feature selection, and a temporal convolutional network (TCN) is used for attack classification.","https://schema.org",{"og:url":101,"og:type":141,"og:title":65,"og:site_name":112,"og:description":66},"article",{"robots":143,"canonical":101},"index,follow",{"doc_id":61,"site_id":75},1790825460]