Abstract
Malware predominantly employs code injections, which allow to run code in the trusted context of another process. This enables malware, for instance, to secretly operate or to intercept critical information. It is crucial for analysts to quickly detect injected code. While there are systems to detect code injections in memory dumps, they suffer from unsatisfying detection rates or their detection granularity is too coarse. In this paper, we present Quincy to overcome these drawbacks. It employs 38 features commonly associated with code injections to classify memory regions. We implemented Quincy for Windows XP, 7 and 10 and compared it to the current state of the art, Volatility’s malfind as well as hollowfind. For this sake, we created a high quality data set consisting of 102 current representatives of code injecting malware families. Quincy improves significantly upon both approaches, with up to 19.49% more true positives and a decrease in false positives by up to 94,76%.
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Barabosch, T., Bergmann, N., Dombeck, A., Padilla, E. (2017). Quincy: Detecting Host-Based Code Injection Attacks in Memory Dumps. In: Polychronakis, M., Meier, M. (eds) Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2017. Lecture Notes in Computer Science(), vol 10327. Springer, Cham. https://doi.org/10.1007/978-3-319-60876-1_10
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DOI: https://doi.org/10.1007/978-3-319-60876-1_10
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