sesa 19(19): e1

Research Article

Adaptive Noise Injection against Side-Channel Attacks on ARM Platform

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  • @ARTICLE{10.4108/eai.25-1-2019.159346,
        author={Naiwei Liu and Wanyu Zang and Songqing Chen and Meng Yu and Ravi Sandhu},
        title={Adaptive Noise Injection against Side-Channel Attacks on ARM Platform},
        journal={EAI Endorsed Transactions on Security and Safety},
        keywords={system security, side-channel attacks, noise injection},
  • Naiwei Liu
    Wanyu Zang
    Songqing Chen
    Meng Yu
    Ravi Sandhu
    Year: 2019
    Adaptive Noise Injection against Side-Channel Attacks on ARM Platform
    DOI: 10.4108/eai.25-1-2019.159346
Naiwei Liu1,*, Wanyu Zang2, Songqing Chen3, Meng Yu2, Ravi Sandhu1
  • 1: Institute for Cyber Security, University of Texas at San Antonio (UTSA), U.S.A
  • 2: Department of Computer Science, Information Technology, and Data Science, Roosevelt University, U.S.A
  • 3: George Mason University, U.S.A
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In recent years, research efforts have been made to develop safe and secure environments for ARM platform. The new ARMv8 architecture brought in security features by design. However, there are still some security problems with ARMv8. For example, on Cortex-A series, there are risks that the system is vulnerable to sidechannel attacks. One major category of side-channel attacks utilizes cache memory to obtain a victim’s secret information. In the cache based side-channel attacks, an attacker measures a sequence of cache operations to obtain a victim’s memory access information, deriving more sensitive information. The success of such attacks highly depends on accurate information about the victim’s cache accesses. In this paper, we describe an innovative approach to defend against side-channel attack on Cortex-A series chips. We also considered the side-channel attacks in the context of using TrustZone protection on ARM. Our adaptive noise injection can significantly reduce the bandwidth of side-channel while maintaining an affordable system overhead. The proposed defense mechanisms can be used on ARM Cortex-A architecture. Our experimental evaluation and theoretical analysis show the effectiveness and efficiency of our proposed defense.