Authors: Adnan Rawashdeh; Mouhammd Alkasassbeh; Muna Al-Hawawreh
Addresses: Software Engineering Department, Faculty of IT & CS, Yarmouk University, Irbid, Jordan ' Computer Science Department, Faculty of IT, Mutah University, Mutah, Jordan ' Computer Science Department, Faculty of IT, Mutah University, Mutah, Jordan
Abstract: 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.
Keywords: distributed denial of service; DDoS; cloud computing; intrusion detection system; hypervisor; attacks detection; neural networks.
International Journal of Computer Applications in Technology, 2018 Vol.57 No.4, pp.312 - 324
Received: 03 Jan 2017
Accepted: 16 May 2017
Published online: 13 Jul 2018 *