Computer architecture simulators play an important role in advancing computer architecture research. With wider research directions and the increased number of simulators that have been developed, it becomes harder to choose a particular simulator to use. This paper reviews the fundamentals of different computer architecture simulation techniques. It also surveys many computer architecture simulators and classifies them into different groups based on their simulation models. Comparing computer architecture simulators with each other and validating their accuracy have been demanding tasks for architects. In addition to providing a survey of computer architecture simulation tools, we measured the experimental error of six contemporary computer architecture simulators: gem5, MARSSx86, Multi2Sim, PTLsim, Sniper, and ZSim. We also performed a detailed comparison of these simulators based on other features such as flexibility and micro-architectural details. We believe that this paper will be a very useful resource for the computer architecture community especially for early-stage computer architecture and systems researchers to gain exposure to the existing architecture simulation options.

This paper presents a run-time detection mechanism for access-driven cache-based Side-Channel Attacks (CSCAs) on Intel's x86 architecture. We demonstrate the detection capability and effectiveness of proposed mechanism on Prime+Probe attcks. The mechanism comprises of multiple machine learning models, which use real-time data from the HPCs for detection. Experiments are performed with two different implementations of AES cryptosystem while under Prime+Probe attack. We provide results under stringent design constraints such as: realistic system load conditions, real-time detection accuracy, speed, system-wide performance overhead and distribution of error (i.e., false positives and negatives) for the used machine learning models. Our results show detection accuracy of > 99% for Prime+Probe attack with performance overhead of 3 − 4% at the highest detection speed, i.e., within 1−2% completion of 4800 AES encryption rounds needed to complete a successful attack.

High resolution and stealthy attacks and their variants such as Flush+Reload, Flush+Flush, Prime+Probe, Spectre and Meltdown have completely exposed the vulnerabilities in Intel's computing architecture over the past few years. Mitigation techniques against such attacks are not very effective for two reasons: 1) Most mitigation techniques protect against a specific vulnerability and do not take a system-wide approach, and 2) they either completely remove or greatly reduce the performance benefits of resource sharing. In this work, we argue in favor of detection-based protection, which would help apply mitigation only after successful detection of the attack at runtime. As such, detection would serve as the first line of defense against such attacks. However, for a detection based protection strategy to be effective, detection needs to be highly accurate, to incur minimum system overhead at runtime, should cover a large set of attacks and be capable of early stage detection, i.e., at the very least before the attack is completed. We propose a machine learning based side-channel attack (SCA) detection tool, called WHISPER that satisfies the above mentioned design constraints. WHISPER uses multiple machine learning models in an Ensemble fashion to detect SCAs at runtime using behavioral data of concurrent processes, that are collected through hardware performance counters (HPCs). Through extensive experiments with different variants of state-of-the-art attacks, we demonstrate that the proposed tool is capable of detecting a large set of known attacks that target both computational and storage parts in computing systems. We present experimental evaluation of WHISPER against Flush+Reload, Flush+Flush, Prime+Probe, Spectre and Meltdown attacks. The results are provided under variable system load conditions and stringent evaluation metrics comprising detection accuracy, speed, system-wide performance overhead and distribution of error (i.e., False Positives & False Negatives). Our experiments show that WHISPER can detect a large and diverse attack vector with more than 99% accuracy at a reasonably low performance overhead.

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This paper presents experimental evaluation and comparative analysis on the use of various Machine Learning (ML) models for detecting Cache-based Side Channel Attacks (CSCAs) in Intel's x86 architecture. The paper provides performance evaluation of ML models based on run-time detection accuracy, speed, computational overhead, and distribution of error in terms of false positives and false negatives. Experiments are performed using state-of-the-art CSCAs namely; Flush+Reload and Flush+Flush attacks, under realistic load conditions on RSA and AES crypto-systems. The paper provides quantitative & qualitative analysis of at least 12 ML models being used for CSCA detection for the first time.