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.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {263882,
title = {Forecasting Malware Capabilities From Cyber Attack Memory Images},
booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)},
year = {2021},
address = {Vancouver, B.C.},
url = {},
publisher = {{USENIX} Association},
month = aug,