research-article

Hypnoguard: Protecting Secrets across Sleep-wake Cycles

Authors:

Mohammad MannanAuthors Info & Claims

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

Pages 945 - 957

https://doi.org/10.1145/2976749.2978372

Published: 24 October 2016 Publication History

Abstract

Attackers can get physical control of a computer in sleep (S3/suspend-to-RAM), if it is lost, stolen, or the owner is being coerced. High-value memory-resident secrets, including disk encryption keys, and private signature/encryption keys for PGP, may be extracted (e.g., via cold-boot or DMA attacks), by physically accessing such a computer. Our goal is to alleviate threats of extracting secrets from a computer in sleep, without relying on an Internet-facing service. We propose Hypnoguard to protect all memory-resident OS/user data across S3 suspensions, by first performing an in-place full memory encryption before entering sleep, and then restoring the plaintext content at wakeup-time through an environment-bound, password-based authentication process. The memory encryption key is effectively "sealed" in a Trusted Platform Module (TPM) chip with the measurement of the execution environment supported by CPU's trusted execution mode (e.g., Intel TXT, AMD-V/SVM). Password guessing within Hypnoguard may cause the memory content to be permanently inaccessible, while guessing without Hypnoguard is equivalent to brute-forcing a high-entropy key (due to TPM protection). We achieved full memory encryption/decryption in less than a second on a mainstream computer (Intel i7-4771 CPU with 8GB RAM, taking advantage of multi-core processing and AES-NI), an apparently acceptable delay for sleep-wake transitions. To the best of our knowledge, Hypnoguard provides the first wakeup-time secure environment for authentication and key unlocking, without requiring per-application changes.

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Index Terms

Hypnoguard: Protecting Secrets across Sleep-wake Cycles
1. Security and privacy

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cover image ACM Conferences

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

October 2016

1924 pages

ISBN:9781450341394

DOI:10.1145/2976749

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Edgar Weippl
SBA Research, Austria
,
Stefan Katzenbeisser
TU Darmstadt, CYSEC, Germany
,
Program Chairs:
Christopher Kruegel
University of California, Santa Barbara, USA
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Andrew Myers
Cornell University, USA
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Shai Halevi
IBM Research, USA

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Published: 24 October 2016

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October 24 - 28, 2016

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Cited By

Zha CXu ZXing JYin H(2024)Loft: An Architecture for Lifetime Management of Privacy Data in Service CooperationUbiquitous Security10.1007/978-981-97-1274-8_17(255-273)Online publication date: 13-Mar-2024
https://doi.org/10.1007/978-981-97-1274-8_17
Franzen FAndreas MHuber MRoussev VThuraisingham BCarminati BKantarcioglu M(2020)FridgeLockProceedings of the Tenth ACM Conference on Data and Application Security and Privacy10.1145/3374664.3375747(215-219)Online publication date: 16-Mar-2020
https://dl.acm.org/doi/10.1145/3374664.3375747
Sartakov VKapitza R(2019)Protecting Secrets of Persistent Systems with Volatility2019 15th European Dependable Computing Conference (EDCC)10.1109/EDCC.2019.00027(89-96)Online publication date: Sep-2019
https://doi.org/10.1109/EDCC.2019.00027
Suzaki KHori YKobara KMannan M(2019)DeviceVeil: Robust Authentication for Individual USB Devices Using Physical Unclonable Functions2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)10.1109/DSN.2019.00041(302-314)Online publication date: Jun-2019
https://doi.org/10.1109/DSN.2019.00041
Huber MHorsch JWessel SGiuffrida CStavrou A(2017)Protecting Suspended Devices from Memory AttacksProceedings of the 10th European Workshop on Systems Security10.1145/3065913.3065914(1-6)Online publication date: 23-Apr-2017
https://dl.acm.org/doi/10.1145/3065913.3065914
Horsch JHuber MWessel S(2017)TransCrypt: Transparent Main Memory Encryption Using a Minimal ARM Hypervisor2017 IEEE Trustcom/BigDataSE/ICESS10.1109/Trustcom/BigDataSE/ICESS.2017.232(152-161)Online publication date: Aug-2017
https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.232

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