• Title/Summary/Keyword: Cache-based Side Channel Attack

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Machine Learning-Based Detection of Cache Side Channel Attack Using Performance Counter Monitor of CPU (Performance Counter Monitor를 이용한 머신 러닝 기반 캐시 부채널 공격 탐지)

  • Hwang, Jongbae;Bae, Daehyeon;Ha, Jaecheol
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.30 no.6
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    • pp.1237-1246
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    • 2020
  • Recently, several cache side channel attacks have been proposed to extract secret information by exploiting design flaws of the microarchitecture. The Flush+Reload attack, one of the cache side channel attack, can be applied to malicious application attacks due to its properties of high resolution and low noise. In this paper, we proposed a detection system, which detects the cache-based attacks using the PCM(Performance Counter Monitor) for monitoring CPU cache activity. Especially, we observed the variation of each counter value of PCM in case of two kinds of attacks, Spectre attack and secret recovering attack during AES encryption. As a result, we found that four hardware counters were sensitive to cache side channel attacks. Our detector based on machine learning including SVM(Support Vector Machine), RF(Random Forest) and MLP(Multi Level Perceptron) can detect the cache side channel attacks with high detection accuracy.

Real-Time Detection of Cache Side-Channel Attacks Using Non-Cache Hardware Events (비 캐시 하드웨어 이벤트를 이용한 캐시 부채널 공격 실시간 탐지)

  • Kim, Hodong;Hur, Junbeom
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.30 no.6
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    • pp.1255-1261
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    • 2020
  • Cache side-channel attack is a class of attacks to retrieve sensitive information from a system by exploiting shared cache resources in CPUs. As the attacks are delivered to wide range of environments from mobile systems to cloud systems recently, many detection strategies have been proposed. Since the conventional cache side-channel attacks are likely to incur tremendous number of cache events, most of the previous detection mechanisms were designed to carefully monitor mostly cache events. However, recently proposed attacks tend to incur less cache events during the attack. PRIME+ABORT attack, for example, leverages the Intel TSX instead of accessing cache to measure access time. Because of the characteristic, attack detection mechanisms based on cache events may hardly detect the attack. In this paper, we conduct an in-depth analysis of the PRIME+ABORT attack to identify the other useful hardware events for detection rather than cache events. Based on our finding, we present a novel mechanism called PRIME+ABORT Detector to detect the PRIME+ABORT attack and demonstrate that the detection mechanism can achieve 99.5% success rates with 0.3% performance overhead.

CacheSCDefender: VMM-based Comprehensive Framework against Cache-based Side-channel Attacks

  • Yang, Chao;Guo, Yunfei;Hu, Hongchao;Liu, Wenyan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.12
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    • pp.6098-6122
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    • 2018
  • Cache-based side-channel attacks have achieved more attention along with the development of cloud computing technologies. However, current host-based mitigation methods either provide bad compatibility with current cloud infrastructure, or turn out too application-specific. Besides, they are defending blindly without any knowledge of on-going attacks. In this work, we present CacheSCDefender, a framework that provides a (Virtual Machine Monitor) VMM-based comprehensive defense framework against all levels of cache attacks. In designing CacheSCDefender, we make three key contributions: (1) an attack-aware framework combining our novel dynamic remapping and traditional cache cleansing, which provides a comprehensive defense against all three cases of cache attacks that we identify in this paper; (2) a new defense method called dynamic remapping which is a developed version of random permutation and is able to deal with two cases of cache attacks; (3) formalization and quantification of security improvement and performance overhead of our defense, which can be applicable to other defense methods. We show that CacheSCDefender is practical for deployment in normal virtualized environment, while providing favorable security guarantee for virtual machines.

Side-Channel Attack of Android Pattern Screen Lock Exploiting Cache-Coherent Interface in ARM Processors (ARM 캐시 일관성 인터페이스를 이용한 안드로이드 OS의 스크린 잠금 기능 부채널 공격)

  • Kim, Youngpil;Lee, Kyungwoon;Yoo, Seehwan;Yoo, Chuck
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.2
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    • pp.227-242
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    • 2022
  • This paper presents a Cache-Coherency Interconnect(CCI)-based Android pattern screen lock(PSL) attack on modern ARM processors. CCI has been introduced to maintain the cache coherency between the big core cluster and the little core cluster. That is, CCI is the central interconnect inside SoC that maintains cache coherency and shares data. In this paper, we reveal that CCI can be a side channel in security, that an adversary can observe security-sensitive operations. We design and implement a technique to compromise Android PSL within only a few attempts using the information of CCI in user-level applications on Android Nougat. Further, we analyzed the relationship between the pattern complexity and security. Our evaluation results show that complex and simple patterns would have similar security strengths against the proposed technique.

Real-Time Detection on FLUSH+RELOAD Attack Using Performance Counter Monitor (Performance Counter Monitor를 이용한 FLUSH+RELOAD 공격 실시간 탐지 기법)

  • Cho, Jonghyeon;Kim, Taehyun;Shin, Youngjoo
    • KIPS Transactions on Computer and Communication Systems
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    • v.8 no.6
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    • pp.151-158
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    • 2019
  • FLUSH+RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH+RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH+RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.