Unpredictable Random Number Generator Based on the Performance Data Helper Interface

Marton, Kinga; Tóth, Peter P.; Suciu, Alin

doi:10.1109/synasc.2012.64

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…In contrast, HPEs have improved our understanding of attacks [7,52], facilitated the evaluation of software components [63], and helped to analyze malware samples [60]. They have also been leveraged to reverse-engineer cache internals on modern processors [42] and to construct random number generators [41,48]. A large class of previous work is dedicated to the real time detection of attacks and malware infections, a selection of which relies on Machine Learning and related techniques.…”

Section: Hardware Performance Eventsmentioning

confidence: 99%

PerfWeb: How to Violate Web Privacy with Hardware Performance Events

Gülmezoğlu

Zankl

Eisenbarth

et al. 2017

Lecture Notes in Computer Science

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The browser history reveals highly sensitive information about users, such as financial status, health conditions, or political views. Private browsing modes and anonymity networks are consequently important tools to preserve the privacy not only of regular users but in particular of whistleblowers and dissidents. Yet, in this work we show how a malicious application can infer opened websites from Google Chrome in Incognito mode and from Tor Browser by exploiting hardware performance events (HPEs). In particular, we analyze the browsers' microarchitectural footprint with the help of advanced Machine Learning techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines, and in contrast to previous literature also Convolutional Neural Networks. We profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing portals, on two machines featuring an Intel and an ARM processor. By monitoring retired instructions, cache accesses, and bus cycles for at most 5 seconds, we manage to classify the selected websites with a success rate of up to 86.3%. The results show that hardware performance events can clearly undermine the privacy of web users. We therefore propose mitigation strategies that impede our attacks and still allow legitimate use of HPEs.

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Section: Hardware Performance Eventsmentioning

confidence: 99%

PerfWeb: How to Violate Web Privacy with Hardware Performance Events

Gülmezoğlu

Zankl

Eisenbarth

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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SoK: The Challenges, Pitfalls, and Perils of Using Hardware Performance Counters for Security

Das

Werner

Antonakakis

et al. 2019

2019 IEEE Symposium on Security and Privacy (SP)

118

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Hardware Performance Counters (HPCs) have been available in processors for more than a decade. These counters can be used to monitor and measure events that occur at the CPU level. Modern processors provide hundreds of hardware events that can be monitored, and with each new processor architecture more are added. Yet, there has been little in the way of systematic studies on how performance counters can best be utilized to accurately monitor events in real-world settings. Especially when it comes to the use of HPCs for security applications, measurement imprecisions or incorrect assumptions regarding the measured values can undermine the offered protection.To shed light on this issue, we embarked on a year-long effort to (i) study the best practices for obtaining accurate measurement of events using performance counters, (ii) understand the challenges and pitfalls of using HPCs in various settings, and (iii) explore ways to obtain consistent and accurate measurements across different settings and architectures. Additionally, we then empirically evaluated the way HPCs have been used throughout a wide variety of papers. Not wanting to stop there, we explored whether these widely used techniques are in fact obtaining performance counter data correctly. As part of that assessment, we (iv) extended the seminal work of Weaver and McKee from almost 10 years ago on non-determinism in HPCs, and applied our findings to 56 papers across various application domains.In that follow-up study, we found the acceptance of HPCs in security applications is in stark contrast to other application areas -especially in the last five years. Given that, we studied an additional representative set of 41 works from the security literature that rely on HPCs, to better elucidate how the intricacies we discovered can impact the soundness and correctness of their approaches and conclusions. Toward that goal, we (i) empirically evaluated how failure to accommodate for various subtleties in the use of HPCs can undermine the effectiveness of security applications, specifically in the case of exploit prevention and malware detection. Lastly, we showed how (ii) an adversary can manipulate HPCs to bypass certain security defenses.

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Unpredictable Random Number Generator Based on the Performance Data Helper Interface

Cited by 2 publications

References 10 publications

PerfWeb: How to Violate Web Privacy with Hardware Performance Events

PerfWeb: How to Violate Web Privacy with Hardware Performance Events

SoK: The Challenges, Pitfalls, and Perils of Using Hardware Performance Counters for Security

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