Autoscopy: Detecting Pattern-Searching Rootkits via Control Flow Tracing

Traditional approaches to rootkit detection assume the execution of code at a privilege level below that of the operating system kernel, with the use of virtual machine technologies to enable the detection system itself to be immune from the virus or rootkit code. In this thesis, we approach the problem of rootkit detection from the standpoint of tracing and instrumentation techniques, which work from within the kernel and also modify the kernel\u27s run-time state to detect aberrant control flows. We wish to investigate the role of emerging tracing frameworks (Kprobes, DTrace etc.) in enforcing operating system security without the reliance on a full-blown virtual machine just for the purposes of such policing. We first build a novel rootkit prototype that uses pattern-searching techniques to hijack hooks embedded in dynamically allocated memory, which we present as a showcase of emerging attack techniques. We then build an intrusion detection system-- autoscopy, atop kprobes, that detects anomalous control flow patterns typically exhibited by rootkits within a running kernel. Furthermore, to validate our approach, we show that we were able to successfully detect 15 existing Linux rootkits. We also conduct performance analyses, which show the overhead of our system to range from 2% to 5% on a wide range of standard benchmarks. Thus by leveraging tracing frameworks within operating systems, we show that it is possible to introduce real-world security in devices where performance and resource constraints are tantamount to security considerations