Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 1Citation - Scopus: 2Spec17Tre: A New Dataset in Hardware Security and Using Deep Learning for Detecting Spectre Attacks(Springer Heidelberg, 2025-05-21) Aktas-Aydin, Hatice; Yalcin, GulayComputer performance has become a significant subject of study due to the processing of big data, the complexity of calculations and the importance of time efficiency. Many companies are improving processor operating principles to increase performance. The most common methods for this purpose are speculative execution and cache usage. While these techniques improve performance, they also introduce certain security vulnerabilities. Spectre is an attack that exploits vulnerabilities created by speculative execution, affecting all modern processor architectures. Research has shown that using machine learning to detect these attacks can be quite effective, although the features are typically gathered at the software level, which may limit detection since some performance parameters are not conveyed to the software. This study presents an analysis of Spectre attacks and their detection using machine learning and deep learning methods at the hardware level. Experiments are conducted using GEM5, a full-system hardware simulator, to ensure that only hardware-visible performance parameters are also collected. Attack detection is performed using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) methods. The LSTM method is used in conjunction with SVM and Convolutional Neural Network (CNN) techniques, and all models were tested on a new dataset, Spec17Tre, created using "519.lbm" from the SPEC CPU2017 benchmarks. The study achieved a 95% accuracy rate in attack detection using the LSTM + CNN hybrid model, which also yielded an F1 score of 0.999 for detecting applied Spectre attack scenarios.Article Citation - WoS: 9Citation - Scopus: 13A Methodology for Comparing the Reliability of GPU-Based and CPU-Based HPCS(Assoc Computing Machinery, 2020-02-06) Cini, Nevin; Yalcin, GulayToday, GPUs are widely used as coprocessors/accelerators in High-Performance Heterogeneous Computing due to their many advantages. However, many researches emphasize that GPUs are not as reliable as desired yet. Despite the fact that GPUs are more vulnerable to hardware errors than CPUs, the use of GPUs in HPCs is increasing more and more. Moreover, due to native reliability problems of GPUs, combining a great number of GPUs with CPUs can significantly increase HPCs' failure rates. For this reason, analyzing the reliability characteristics of GPU-based HPCs has become a very important issue. Therefore, in this study we evaluate the reliability of GPU-based HPCs. For this purpose, we first examined field data analysis studies for GPU-based and CPU-based HPCs and identified factors that could increase systems failure/error rates. We then compared GPU-based HPCs with CPU-based HPCs in terms of reliability with the help of these factors in order to point out reliability challenges of GPU-based HPCs. Our primary goal is to present a study that can guide the researchers in this field by indicating the current state of GPU-based heterogeneous HPCs and requirements for the future, in terms of reliability. Our second goal is to offer a methodology to compare the reliability of GPU-based HPCs and CPU-based HPCs. To the best of our knowledge, this is the first survey study to compare the reliability of GPU-based and CPU-based HPCs in a systematic manner.