服务拒绝攻击
试验台
计算机科学
软件定义的网络
云计算
应用层DDoS攻击
前进飞机
洪水(心理学)
计算机网络
物联网
特里诺
钥匙(锁)
计算机安全
分布式计算
互联网
操作系统
网络数据包
心理治疗师
心理学
作者
D. Kavitha,R. Ramalakshmi
标识
DOI:10.1016/j.jfranklin.2024.107197
摘要
Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.
科研通智能强力驱动
Strongly Powered by AbleSci AI