Alireza Sadeghpour | Searcharxiv

arXiv2023

Comprehensive Evaluation of RSB and Spectre Vulnerability on Modern Processors

Performance-enhancing mechanisms such as branch prediction, out-of-order execution, and return stack buffer (RSB) have been widely employed in today's modern processing units. Although successful in increasing the CPU performance, exploiting the design flaws and security bugs in these components have set the background for various types of microarchitectural attacks such as Spectre and Meltdown. While many attacks such as Meltdown and Spectre have been numerously implemented and analyzed on Intel processors, few researches have been carried out to evaluate their impact on ARM processors. Moreover, SpectreRSB vulnerability, the newer variant of spectre attack based on RSB, has been neglected in recent studies. In this work, we first evaluate the SpectreRSB vulnerability by implementing this attack on ARM processors, which, to the best of our knowledge, has not been implemented and analyzed on ARM processors. We further present a security evaluation of ARM processors by implementing different variants of Spectre-family attacks. By analyzing the results obtained from various experiments, we evaluate ARM processors security regarding their diverse microarchitectural designs. We also introduce a high throughput and noise-free covert channel, based on the RSB structure. Based on our experiments, the throughput of the covert channel is 94.19KB/s with negligible error.

cs.CR↗

arXiv2019

Randomized Predictive P-values: A Versatile Model Diagnostic Tool with Unified Reference Distribution

Examining residuals such as Pearson and deviance residuals, is a standard tool for assessing normal regression. However, for discrete response, these residuals cluster on lines corresponding to distinct response values. Their distributions are far from normality; graphical and quantitative inspection of these residuals provides little information for model diagnosis. Marshall and Spiegelhalter (2003) defined a cross-validatory predictive p-value for identifying outliers. Predictive p-values are uniformly distributed for continuous response but not for discrete response. We propose to use randomized predictive p-values (RPP) for diagnosing models with discrete responses. RPPs can be transformed to "residuals" with normal distribution, called NRPPs by us. NRPPs can be used to diagnose all regression models with scalar response using the same way for diagnosing normal regression. The NRPPs are nearly the same as the randomized quantile residuals (RQR), which are previously proposed by Dunn and Smyth (1996) but remain little known by statisticians. This paper provides an exposition of RQR using the RPP perspective. The contributions of this exposition include: (1) we give a rigorous proof of uniformity of RPP and illustrative examples to explain the uniformity under the true model; (2) we conduct extensive simulation studies to demonstrate the normality of NRPPs under the true model; (3) our simulation studies also show that the NRPP method is a versatile diagnostic tool for detecting many kinds of model inadequacies due to lack of complexity. The effectiveness of NRPP is further demonstrated with a health utilization dataset.

stat.ME↗