A Comprehensive Survey On Detection And Mitigation Of DDoS Attacks Enabled With Deep Learning Techniques In Cloud Computing

To detect the DDoS (Distributed Denial of Service) attack that initiates a flooding attack over a targeted server or service meanwhile causing network traffic and disrupting legitimate users from accessing the network is significant for reducing the hosting issues and time loss. Additionally, DDoS attacks have become the foremost attack in cloud computing and cause complexity in detection and mitigation appropriately as the attacks make the server down which results in the user to loss access to the Internet and causing financial loss to the cloud computing organizations. Despite the numerous solutions that exist in the current era, the attacks continue to grow in volume as well as severity. As a consequence, multiple researchers have formulated for securing the cloud servers with the help of a software-defined network(SDN) to identify and eliminate the impacts of DDoS attacks. In this research, an in-depth analysis of the DDoS detection and mitigation frameworks for securing the servers in cloud computing is exhibited to analyze the security and authentication of attack detection systems in cloud applications. A well-defined and prominent solution can be formulated by investigators through this detailed investigation based on different datasets and techniques. The research elucidates various techniques in the detection and mitigation of DDoS attacks along with their advantages, disadvantages, and research gaps, which supports the researchers in attaining deep knowledge of the techniques prevalent in DDoS attack detection in cloud networks.