Degree

Doctor of Philosophy (PhD)

Department

Computer Science

Document Type

Dissertation

Abstract

Memory forensics is an important tool in the hands of investigators. However, determining if a computer is infected with malicious software is time consuming, even for experts. Tasks that require manual reverse engineering of code or data structures create a significant bottleneck in the investigative workflow. Through the application of emulation software and symbolic execution, these strains have been greatly lessened, allowing for faster and more thorough investigation. Furthermore, these efforts have reduced the barrier for forensic investigation, so that reasonable conclusions can be drawn even by non-expert investigators. While previously Volatility had allowed for the detection of malicious hooks and injected code with an insurmountably high false positive rate, the techniques presented in the work have allowed for a much lower false positive rate automatically, and yield more detailed information when manual analysis is required. The second contribution of this work is to improve the reliability of memory forensic tools. As it currently stands, if some component of the operating system or language runtime has been updated, the task of verifying that these changes do not affect the correctness of investigative tools involves a large reverse engineering effort, and significant domain knowledge, on the part of whoever maintains the tool. Through modifications of the techniques used in the hook analysis, this burden can be lessened or eliminated by comparing the last known functionality to the new functionality. This allows the tool to be updated quickly and effectively, so that investigations can proceed without issue.

Date

3-30-2021

Committee Chair

Richard III, Golden

DOI

10.31390/gradschool_dissertations.5528

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