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LDX: Causality Inference by Lightweight Dual Execution

Authors:

William Nick Sumner,

Brendan Saltaformaggio,

Dongyan XuAuthors Info & Claims

ACM SIGPLAN Notices, Volume 51, Issue 4

Pages 503 - 515

https://doi.org/10.1145/2954679.2872395

Published: 25 March 2016 Publication History

Abstract

Causality inference, such as dynamic taint anslysis, has many applications (e.g., information leak detection). It determines whether an event e is causally dependent on a preceding event c during execution. We develop a new causality inference engine LDX. Given an execution, it spawns a slave execution, in which it mutates c and observes whether any change is induced at e. To preclude non-determinism, LDX couples the executions by sharing syscall outcomes. To handle path differences induced by the perturbation, we develop a novel on-the-fly execution alignment scheme that maintains a counter to reflect the progress of execution. The scheme relies on program analysis and compiler transformation. LDX can effectively detect information leak and security attacks with an average overhead of 6.08% while running the master and the slave concurrently on separate CPUs, much lower than existing systems that require instruction level monitoring. Furthermore, it has much better accuracy in causality inference.

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Index Terms

LDX: Causality Inference by Lightweight Dual Execution
1. Security and privacy
  1. Systems security
    1. Information flow control
2. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Runtime environments
      2. Source code generation

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cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 51, Issue 4

ASPLOS '16

April 2016

774 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2954679

Editor:
Andy Gill
University of Kansas, Lawrence, KS

Issue’s Table of Contents

ASPLOS '16: Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems
March 2016
824 pages
ISBN:9781450340915
DOI:10.1145/2872362
General Chair:
Tom Conte
Georgia Tech, USA
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Program Chair:
Yuanyuan Zhou
University of California, San Diego, USA

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Published: 25 March 2016

Published in SIGPLAN Volume 51, Issue 4

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Zhu TYu JXiong CCheng WYuan QYing JChen TZhang JLv MChen YWang TFan Y(2023)APTSHIELD: A Stable, Efficient and Real-Time APT Detection System for Linux HostsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.324366720:6(5247-5264)Online publication date: Nov-2023
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Inam MChen YGoyal ALiu JMink JMichael NGaur SBates AHassan W(2023)SoK: History is a Vast Early Warning System: Auditing the Provenance of System Intrusions2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179405(2620-2638)Online publication date: May-2023
https://doi.org/10.1109/SP46215.2023.10179405
Wang JWei JPang JZhang FLi S(2022)Security Enhancement Through Compiler-Assisted Software Diversity With Deep Reinforcement LearningInternational Journal of Digital Crime and Forensics10.4018/IJDCF.30287814:2(1-18)Online publication date: 17-Jun-2022
https://dl.acm.org/doi/10.4018/IJDCF.302878
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