TEE-aided Write Protection Against Privileged Data Tampering
May 26, 2019 ยท Declared Dead ยท ๐ Network and Distributed System Security Symposium
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Lianying Zhao, Mohammad Mannan
arXiv ID
1905.10723
Category
cs.CR: Cryptography & Security
Citations
17
Venue
Network and Distributed System Security Symposium
Last Checked
3 months ago
Abstract
Unauthorized data alteration has been a longstanding threat since the emergence of malware. System and application software can be reinstalled and hardware can be replaced, but user data is priceless in many cases. Especially in recent years, ransomware has become high-impact due to its direct monetization model. State-of-the-art defenses are mostly based on known signature or behavior analysis, and more importantly, require an uncompromised OS kernel. However, malware with the highest software privileges has shown its obvious existence. We propose to move from current detection/recovery based mechanisms to data loss prevention, where the focus is on armoring data instead of counteracting malware. Our solution, Inuksuk, relies on today's Trusted Execution Environments (TEEs), as available both on the CPU and storage device, to achieve programmable write protection. We back up a copy of user-selected files as write-protected at all times, and subsequent updates are written as new versions securely through TEE. We implement Inuksuk on Windows 7 and 10, and Linux (Ubuntu); our core design is OS and application agnostic, and incurs no run-time performance penalty for applications. File transfer disruption can be eliminated or alleviated through access modes and customizable update policies (e.g., interval, granularity). For Inuksuk's adoptability in modern OSes, we have also ported Flicker (EuroSys 2008), a defacto standard tool for in-OS privileged TEE management, to the latest 64-bit Windows.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Membership Inference Attacks against Machine Learning Models
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Practical Black-Box Attacks against Machine Learning
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Extracting Training Data from Large Language Models
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted