An Investigation on Distributed Denial of Service Attack in Edge Computing

The data-driven industry has benefited greatly from the edge computing platform. The general architecture places data storage and computation close to the source of the data. Recent industrial applications and machine learning environments necessitate fast data transmission to a centralized data center for further processing. As a result, clients expect low latency when collecting and processing the data. Data processing can utilize edge computing in three different ways. Bots, distributed denial of service attacks, artificial intelligence and machine learning are all examples of DDoS attacks. The goal of DDoS is to impede the availability of network-connected systems. They generate excessive traffic to the targeted servers will deplete the computational and communication resources. Edge computing provides substantial assistance to lightweight devices to complete a complex task. According to the statistics, the attacks are distiibuted denial of service, malware, side channel attacks, authorization and authentication attacks. This research work examines the Distributed denial of service attacks on cloud and fog computing, which are similar to edge computing. They offer what appear to be effective solutions to the threat. This study also discusses about the current research challenges and future directions in this field.