Cloud computing is a revolution in IT technology that provides scalable, virtualized on-demand resources to the end users with greater flexibility, less maintenance and reduced infrastructure cost. These resources are supervised by different management organizations and provided over Internet using known networking protocols, standards and formats. The underlying technologies and legacy protocols contain bugs and vulnerabilities that can open doors for intrusion by the attackers. Attacks as DDoS (Distributed Denial of Service) are ones of the most frequent that inflict serious damage and affect the cloud performance. In a DDoS attack, the attacker usually uses innocent compromised computers (called zombies) by taking advantages of known or unknown bugs and vulnerabilities to send a large number of packets from these already-captured zombies to a server. This may occupy a major portion of network bandwidth of the victim cloud infrastructures or consume much of the servers time. Thus, in this work, we designed a DDoS detection system based on the C.4.5 algorithm to mitigate the DDoS threat. This algorithm, coupled with signature detection techniques, generates a decision tree to perform automatic, effective detection of signatures attacks for DDoS flooding attacks. To validate our system, we selected other machine learning techniques and compared the obtained results.

In a typical healthcare monitoring system, the cloud is the preferred platform to aggregate, store and analyse data collected from Medical Internet of Things (or MIoT) devices. However, remote cloud servers and storage can be a source of substantial delay. To overcome such delays, an intermediate layer of fog or edge nodes is used for localised processing and storage of MIoT data. To this end, an integrated architecture consisting of MIoT devices, fog and cloud computing has now become the most preferred solution for a healthcare monitoring system. In this study, we propose an analytical model of such a system and use it to show how to reduce the cost for computing resources while guaranteeing performance constraints. The proposed analytical model is based on network of queues and has the ability to estimate the minimum required number of computing resources to meet the service level agreement. The authors verify and cross‐validate the analytical model through Java modelling tools discrete event simulator. Results obtained from analysis and simulation show that the proposed model, under different workload conditions, can predict the system response time, and can accurately determine the number of computing resources needed for health data services to achieve desired performance.