AI

ShellOS: Enabling fast detection and forensic analysis of code injection attacks

Abstract

The availability of off-the-shelf exploitation toolkits for compromising hosts, coupled with the rapid rate of exploit discovery and disclosure, has made exploit or vulnerability-based detection far less effective than it once was. For instance, the increasing use of metamorphic and polymorphic techniques to deploy code injection attacks continues to confound signature-based detection techniques. The key to detecting these attacks lies in the ability to discover the presence of the injected code (or, shellcode). One promising technique for doing so is to examine data (be that from network streams or buffers of a process) and efficiently execute its content to find what lurks within. Unfortunately, current approaches for achieving this goal are not robust to evasion or scalable, primarily because of their reliance on software-based CPU emulators. In this paper, we argue that the use of software-based emulation techniques are not necessary, and instead propose a new framework that leverages hardware virtualization to better enable the detection of code injection attacks. We also report on our experience using this framework to analyze a corpus of malicious Portable Document Format (PDF) files and network-based attacks.