FQBDDA: fuzzy Q-learning based DDoS attack detection algorithm for cloud computing environment

With on-demand resources, flexibility, scalability, dynamic nature, and cheaper maintenance costs, cloud computing technology has revolutionized the Information Technology sector, and almost everyone using the internet relies in some manner on the use of cloud services. Distributed denial of service (DDoS) attack blocks the services by flooding high or low volumes of malicious traffic to exhaust the servers, resources, etc. of the Cloud environment. In today’s era, they are challenging to detect because of low-rate traffic and its hidden approach in the cloud. Studying all DDoS attacks with their possible solution is essential to protect the cloud computing environment. In this paper, we have proposed a fuzzy Q learning algorithm and Chebyshev’s Inequality principle to counter the problem of DDoS attacks. The proposed framework follows the inclusion of Chebyshev’s inequality for workload prediction in the cloud in the analysis phase and fuzzy Q-learning in the planning phase. Experimental results prove that our proposed fuzzy Q-learning based DDoS attack detection algorithm for cloud computing environment (FQBDDA) model prevent DDoS attack.