Forecasting Malware Capabilities From Cyber Attack Memory Images


Omar Alrawi, Moses Ike, Matthew Pruett, Ranjita Pai Kasturi, Srimanta Barua, Taleb Hirani, Brennan Hill, and Brendan Saltaformaggio, Georgia Institute of Technology


The remediation of ongoing cyber attacks relies upon timely malware analysis, which aims to uncover malicious functionalities that have not yet executed. Unfortunately, this requires repeated context switching between different tools and incurs a high cognitive load on the analyst, slowing down the investigation and giving attackers an advantage. We present Forecast, a post-detection technique to enable incident responders to automatically predict capabilities which malware have staged for execution. Forecast is based on a probabilistic model that allows Forecast to discover capabilities and also weigh each capability according to its relative likelihood of execution (i.e., forecasts). Forecast leverages the execution context of the ongoing attack (from the malware's memory image) to guide a symbolic analysis of the malware's code. We performed extensive evaluations, with 6,727 real-world malware and futuristic attacks aiming to subvert Forecast, showing the accuracy and robustness in predicting malware capabilities.

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@inproceedings {263882,
author = {Omar Alrawi and Moses Ike and Matthew Pruett and Ranjita Pai Kasturi and Srimanta Barua and Taleb Hirani and Brennan Hill and Brendan Saltaformaggio},
title = {Forecasting Malware Capabilities From Cyber Attack Memory Images},
booktitle = {30th USENIX Security Symposium (USENIX Security 21)},
year = {2021},
isbn = {978-1-939133-24-3},
pages = {3523--3540},
url = {},
publisher = {USENIX Association},
month = aug

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