Virtualization brings the benefits of utilization and scalability to the multi-tenant cloud platforms. However, the hypervisor, as one of the foundations in virtualization, is challenging to survive under various malicious attacks due to its large attack surface. This article presents a novel group detection framework for the hypervisor code integrity, called Outlier . In an Outlier group, each host contains two parts. One is a distributed detection protocol, called Co-protocol , and the other is a detection interface, called Checker . The Co-protocol constructs trust for the integrity detection within an Outlier group. With the Co-protocol, each Checker conducts reliable integrity detection on the hypervisor code, and then the potential “outlier” host is perceived. We implement our Outlier prototype on the Xen hypervisor and evaluate its overhead. Experiments show that the introduction of the Outlier has few impacts on the performance of the virtualized systems.

Cloud computing is convenient to provide adequate resources for tenants. However, since multiple tenants share the underlying hardware resources, malicious tenants can use the shared processor to launch cache-based attacks. Such attacks can help malicious tenants steal private data of other tenants bypassing isolation mechanisms provided by the system, resulting in information leakage. Moreover, Spectre and Meltdown vulnerabilities can even extract memory contents arbitrarily with the help of cache attacks. Therefore, cache-based attacks pose a serious threat to the security of cloud platforms. To defeat such attacks, many detection methods have been proposed. However, most methods induce high false positives because they completely rely on the hardware performance counters (HPCs) and detect attacks with static criteria. To solve this problem, this article proposes a self-feedback detector named CBA-Detector to detect cache-based attacks in real time. Specifically, CBA-Detector first uses machine learning technologies to create models for identifying suspicious programs with abnormal hardware behaviors, then analyzes suspicious programs from the instruction level to identify real attacks and provide feedback. Based on the feedback, the models can be updated to further improve their detection accuracy. As our experiments show, CBA-Detector can accurately identify cache-based attacks in real time and introduces a little overhead. Besides, the misjudgment rate decreases with the running time.