Retrofitting LBR Profiling to Enhance Virtual Machine Introspection

Cloud attack provenance is a well-established industrial practice for assuring transparency and accountability for a service provider to tenants. However, the multi-tenancy and self-service nature coupled with the sheer size of a cloud implies many unique challenges to cloud forensics. Although Virtual Machine Introspection (VMI) is a powerful tool for attack provenance due to the privilege isolation, the stealthiness of state-of-the-art attacks and the lack of precise information make existing attack provenance solutions difficult to fulfill real-time forensics when tracking enormous suspicious behaviors. To this end, we propose an instruction-level tracing framework for inspecting the presence of attacks by dynamically tracking shared processor hardware event patterns and analyzing the attack traces. To overcome the challenges of real-time detection and provenance, we advocate Last Branch Record (LBR) profiling, to extract the suspicious execution flows. With the hardware assistance and software-based virtualization introspection, we show that the framework can provide an effective response to threats in different cases, thereby enabling a quick attack provenance with high fidelity. The evaluation shows that our prototype introduces negligible performance penalties.