An anomaly-based approach for DDoS attack detection in cloud environment

Cloud computing is currently a major focal point for researchers owing to its widespread application and benefits. Cloud computing's complete reliance on the internet for service provision and its distributed nature pose challenges to security, the most serious being insider Distributed Denial of Service (DDoS) which causes a total deactivation of service. Traditional defence mechanisms, such as firewalls, are unable to detect insider attacks. This work proposes an anomaly intrusion detection approach in the hypervisor layer to discourage DDoS activities between virtual machines. The proposed approach is implemented by the evolutionary neural network which integrates the particle swarm optimisation with neural network for detection and classification of the traffic that is exchanged between virtual machines. The performance analysis and results of our proposed approach detect and classify the DDoS attacks in the cloud environment with minimum false alarms and high detection accuracy.