HookTracer: A System for Automated and Accessible API Hooks Analysis. (July 2019)

• Record Type: Journal Article
• Authors: Case, Andrew; Jalalzai, Mohammad M.; Firoz-Ul-Amin, Md; Maggio, Ryan D.; Ali-Gombe, Aisha; Sun, Mingxuan; Richard, Golden G.
• Is Part Of: Digital investigation. Volume 29(2019)Supplement
• Page Start: S104
• Page End: S112
• Publication Date: 2019-07
• Subjects: Memory forensics -- Malware -- Memory analysis -- API hooks -- Unicorn -- Emulation; Forensic sciences -- Data processing -- Periodicals; Criminal investigation -- Data processing -- Periodicals; 363.250285
• DOI: 10.1016/j.diin.2019.04.011
• Languages: English
• ISSNs: 1742-2876

Abstract

Abstract: The use of memory forensics is becoming commonplace in digital investigation and incident response, as it provides critically important capabilities for detecting sophisticated malware attacks, including memory-only malware components. In this paper, we concentrate on improving analysis of API hooks, a technique commonly employed by malware to hijack the execution flow of legitimate functions. These hooks allow the malware to gain control at critical times and to exercise complete control over function arguments and return values. Existing techniques for detecting hooks, such the Volatility plugin apihooks, do a credible job, but generate numerous false positives related to non-malicious use of API hooking. Furthermore, deeper analysis to determine the nature of hooks detected by apihooks typically requires substantial skill in reverse engineering and an extensive knowledge of operating systems internals. In this paper, we present a new, highly configurable tool called hooktracer, which eliminates false positives, provides valuable insight into the operation of detected hooks, and generates portable signatures called hook traces, which can be used to rapidly investigate large numbers of machines for signs of malware infection.