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.
The need to protect resources against attackers is reflected by huge information security investments of firms worldwide. In the presence of budget constraints and a diverse set of assets to protect, organizations have to decide in which IT security measures to invest, how to evaluate those investment decisions, and how to learn from past decisions to optimize future security investment actions. While the academic literature has provided valuable insights into these issues, there is a lack of empirical contributions. To address this lack, we conduct a theory-based exploratory multiple case study. Our case study reveals that (1) firms' investments in information security are largely driven by external environmental and industry-related factors, (2) firms do not implement standardized decision processes, (3) the security process is perceived to impact the business process in a disturbing way, (4) both the implementation of evaluation processes and the application of metrics are hardly existent and (5) learning activities mainly occur at an ad-hoc basis.
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.
The protection of assets, including IT resources, intellectual property and business processes, against security attacks has become a challenging task for organizations. From an economic perspective, rms need to minimize the probability of a successful security incident or attack while staying within the boundaries of their information security budget in order to optimize their investment strategy. In this paper, an optimization model to support information security investment decision-making in organizations is proposed considering the two con icting objectives (simultaneously minimizing the costs of countermeasures while maximizing the security level). Decision models that support the rms' decisions considering the trade-o between the security level and the investment allocation are bene cial for organizations to facilitate and justify security investment choices. CCS CONCEPTS• Security and privacy → Systems security; KEYWORDSInformation security investment, decision-making, multi-objective optimization ACM
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