Combating the OS-level malware is a very challenging problem as this type of malware can compromise the operating system, obtaining the kernel privilege and subverting almost all the existing anti-malware tools. This work aims to address this problem in the context of mobile devices. As real-world malware is very heterogeneous, we narrow down the scope of our work by especially focusing on a special type of OS-level malware that always corrupts user data. We have designed mobiDOM, the first framework that can combat the OS-level data corruption malware for mobile computing devices. Our mobiDOM contains two components, a malware detector and a data repairer. The malware detector can securely and timely detect the presence of OS-level malware by fully utilizing the existing hardware features of a mobile device, namely, flash memory and Arm TrustZone. Specifically, we integrate the malware detection into the flash translation layer (FTL), a firmware layer embedded into the flash storage hardware, which is inaccessible to the OS; in addition, we run a trusted application in the Arm TrustZone secure world, which acts as a user-level manager of the malware detector. The FTL-based malware detection and the TrustZone-based manager can communicate with each other stealthily via steganography. The data repairer can allow restoring the external storage to a healthy historical state by taking advantage of the out-of-place-update feature of flash memory and our malware-aware garbage collection in the FTL. Security analysis and experimental evaluation on a real-world testbed confirm the effectiveness of mobiDOM.
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MAB-Malware: A Reinforcement Learning Framework for Blackbox Generation of Adversarial Malware
- Award ID(s):
- 1719175
- NSF-PAR ID:
- 10382411
- Date Published:
- Journal Name:
- ASIA CCS '22: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security
- Page Range / eLocation ID:
- 990 to 1003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3488932.3497768
