Biblio

In Cloud Computing Environment, using only static security measures didn't mitigate the attack considerably. Hence, deployment of sophisticated methods by the attackers to understand the network topology of complex network makes the task easier. For this reason, the use of dynamic security measure as virtual machine (VM) migration increases uncertainty to locate a virtual machine in a dynamic attack surface. Although this, not all VM's migration enhances security. Indeed, the destination server to host the VM should be selected precisely in order to avoid externality and attack at the same time. In this paper, we model migration in cloud environment by using continuous Markov Chain. Then, we analyze the probability of a VM to be compromised based on the destination server parameters. Finally, we provide some numerical results to show the effectiveness of our approach in term of avoiding intrusion.

Cloud Data Center (CDC) security remains a major challenge for business organizations and takes an important concern with research works. The attacker purpose is to guarantee the service unavailability and maximize the financial loss costs. As a result, Distributed Denial of Service (DDoS) attacks have appeared as the most popular attack. The main aim of such attacks is to saturate and overload the system network through a massive data packets size flooding toward a victim server and to block the service to users. This paper provides a defending system in order to mitigate the Denial of Service (DoS) attack in CDC environment. Basically it outlines the different techniques of DoS attacks and its countermeasures by combining the filtering and detection mechanisms. We presented an analytical model based on queueing model to evaluate the impact of flooding attack on cloud environment regarding service availability and QoS performance. Consequently, we have plotted the response time, throughput, drop rate and resource computing utilization varying the attack arrival rate. We have used JMT (Java Modeling Tool) simulator to validate the analytical model. Our approach was appeared powerful for attacks mitigation in the cloud environment.