In the last few years, research has been motivated to provide a categorization and classification of security concerns accompanying the growing adaptation of Infrastructure as a Service (IaaS) clouds. Studies have been motivated by the risks, threats and vulnerabilities imposed by the components within the environment and have provided general classifications of related attacks, as well as the respective detection and mitigation mechanisms. Virtual Machine Introspection (VMI) has been proven to be an effective tool for malware detection and analysis in virtualized environments. In this paper, we classify attacks in IaaS cloud that can be investigated using VMI-based mechanisms. This infers a special focus on attacks that directly involve Virtual Machines (VMs) deployed in an IaaS cloud. Our classification methodology takes into consideration the source, target, and direction of the attacks. As each actor in a cloud environment can be both source and target of attacks, the classification provides any cloud actor the necessary knowledge of the different attacks by which it can threaten or be threatened, and consequently deploy adapted VMI-based monitoring architectures. To highlight the relevance of attacks, we provide a statistical analysis of the reported vulnerabilities exploited by the classified attacks and their financial impact on actual business processes.
Public and private Infrastructure as a Service (IaaS) clouds are widely used by individuals and organizations to provision flexible virtual computing resources on demand. Virtual Network Embedding (VNE) algorithms are employed in this context to provide an automated resource assignment. With multiple involved parties, security-aware Virtual Machine (VM) placement becomes highly relevant for production environments. Moreover, VNE algorithms should also consider the security requirements of the interconnections between VMs, thereby extending the problem to networks. This paper discusses security requirements of Virtual Networks (VNs) and shows how they can be modeled in VNE to map them to the provided security mechanisms in the physical network. The paper also presents an implementation of this security-aware VNE model in the public simulation platform ALEVIN, demonstrating the applicability with a realistic use case of such a model.
Abstract. Due to the proliferation of cloud computing, cloud-based systems are becoming an increasingly attractive target for malware. In an Infrastructure-as-a-Service (IaaS) cloud, malware located in a customer's virtual machine (VM) affects not only this customer, but may also attack the cloud infrastructure and other co-hosted customers directly. This paper presents CloudIDEA, an architecture that provides a security service for malware defense in cloud environments. It combines lightweight intrusion monitoring with on-demand isolation, evidence collection, and in-depth analysis of VMs on dedicated analysis hosts. A dynamic decision engine makes on-demand decisions on how to handle suspicious events considering cost-efficiency and quality-of-service constraints.
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