PDL Abstract

Efficient Hypervisor Based Malware Detection

Ph.D. Dissertation, Carnegie Mellon University, Electrical and Computer Engineering, May 2015.

Peter Friedrich Klemperer

Electrical and Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213


Recent years have seen an uptick in master boot record (MBR) based rootkits that load before the Windows operating system and subvert the operating system’s own procedures. As such, MBR rootkits are difficult to counter with operating system-based antivirus software that runs at the same privilege-level as the rookits. Hypervisors operate at a higher privilege level than the guests they manage, creating a high-ground position in the host. This high-ground position can be exploited to perform security checks on the virtual machine guests where the checking software is isolated from guest-based viruses. The efficient introspection system described in this thesis targets existing virtualized systems to improve security with real-time, concurrent memory introspection capabilities. Efficient introspection decouples memory introspection from virtual machine guest execution, establishes coherent and consistent memory views between the host and running guest, while maintaining normal guest operation. Existing introspection systems have provided one or two of these properties but not all three at once.

This thesis presents a new concurrent-computing approach – high-performance memory snapshotting – to accelerating hypervisor based introspection of virtual machine guest memory that combines all three elements to improve performance and security. Memory snapshots create a coherent and consistent memory view of the guest that can be shared with the independently running introspection application. Three memory snapshotting mechanisms are presented and evaluated for their impact on normal guest operation.

Existing introspection systems and security protection techniques that were previously dismissed as too slow are now be enabled by efficient introspection. This thesis explains why existing introspection systems are inadequate, describes how existing system performance can be improved, evaluates an efficient introspection prototype on both applications and microbenchmarks, and discusses two potential security applications that are enabled by efficient introspection. These applications point to efficient introspection’s utility for supporting useful security applications.