A Distributed Denial of Service (DDoS) attack is an organised distributed packet-storming technique that aims to overload network devices and the communication channels between them. Its major objective is to prevent legitimate users from accessing networks, servers, services, or other computer resources. In this thesis, we propose, implement and evaluate a DDoS Detector approach consisting of detection, defence and knowledge sharing components. The detection component is designed to detect known and unknown DDoS attacks using an Artificial Neural Network (ANN) while the defence component prevents forged DDoS packets from reaching the victim. DDoS Detectors are distributed across one or more networks in order to mitigate the strength of a DDoS attack. The knowledge sharing component uses encrypted messages to inform other DDoS Detectors when it detects a DDoS attack. This mechanism increases the efficacy of the detection mechanism between the DDoS Detectors. This approach has been evaluated and tested against other related approaches in terms of Sensitivity, Specificity, False Positive Rate (FPR), Precision, and Detection Accuracy. A major contribution of the research is that this approach achieves a 98% DDoS detection and mitigation accuracy, which is 5% higher than the best result of previous related approaches.