Detection of Distributed Denial of Service (DDOS) Attacks Using Artificial Neural Networks on Cloud

This dissertation proposes a technique for detecting a significant threat to the availability of cloud services. By definition, a Distributed Denial of Service Attack (DDoS) refers to an attack in which multiple systems compromised by Trojan are maliciously used to target a single system. The attack leads to the denial of a particular service on the target system. In a DDoS attack, the target system and the systems used to perform the attack are all victims of the action. First, we present a survey of the various mechanisms, both traditional and modern, that are applied in detecting cloud-based DDoS attacks. We then propose a DDoS detection system using artificial neural networks that will detect known and unknown DDoS attacks with integration with signatures approach. The proposed method has two major subsystems: (1) Data collection: a traffic generator has been developed to collect data corresponding to different DDoS types, and (2) distributed DDoS detection: two different approaches are used; a neural network algorithm, as anomaly- based detection and signature based detection. The Amazon public cloud was used for running the fast cluster engine with varying cores of machines. The experiment results achieved the highest accuracy and detection rate compared to signature-based or neural networks-based approach. The findings in this research can be extended to allow the application of the proposed technology for bigger network traffic