Hoda Naghibijouybari - Academia.edu (original) (raw)
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Papers by Hoda Naghibijouybari
2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021
Author(s): Naghibijouybari, Hoda | Advisor(s): Abu-Ghazaleh, Nael | Abstract: Modern computing pl... more Author(s): Naghibijouybari, Hoda | Advisor(s): Abu-Ghazaleh, Nael | Abstract: Modern computing platforms are becoming increasingly heterogeneous, combining a main processor with accelerators/co-processors to perform data-intensive computations. As the most common accelerator, Graphics Processing Units (GPUs) are widely integrated in all computing devices to enhance the performance of both graphics and computational workloads. GPUs as new components in heterogeneous systems introduce potential vulnerabilities and other security problems.This dissertation studies the security of modern GPUs in terms of micro-architectural covert and side channels attacks and defenses. In micro-architectural attacks, information leakage is measured through processes interactions through the shared hardware resources on a processor.The first contribution of my dissertation is a study of covert channel attacks on General Purpose GPUs (GPGPUs). I first reverse engineer the hardware scheduler to create co-...
Machine learning (ML), and deep learning in particular, have become a critical workload as they a... more Machine learning (ML), and deep learning in particular, have become a critical workload as they are becoming increasingly applied at the core of a wide range of application spaces. Computer systems, from the architecture up, have been impacted by ML in two primary directions: (1) ML is an increasingly important computing workload, with new accelerators and systems targeted to support both training and inference at scale; and (2) ML supporting computer system decisions, both during design and run times, with new machine learning based algorithms controlling systems to optimize their performance, reliability and robustness. In this paper, we will explore the intersection of security, ML and computing systems, identifying both security challenges and opportunities. Machine learning systems are vulnerable to new attacks including adversarial attacks crafted to fool a classifier to the attacker's advantage, membership inference attacks attempting to compromise the privacy of the trai...
Proceedings of the ACM International Conference on Supercomputing, 2019
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018
Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture, 2017
IEEE Transactions on Dependable and Secure Computing, 2019
IEEE Computer Architecture Letters, 2017
International Journal of Computer Applications, 2015
IEEE Design & Test, 2021
Editor’s notes: This article demonstrates that architectural side-channel attacks are possible al... more Editor’s notes: This article demonstrates that architectural side-channel attacks are possible also on applications that use the GPUs. —Rosario Cammarota, Intel Labs —Francesco Regazzoni, University of Amsterdam and Università della Svizzera Italiana
2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021
Author(s): Naghibijouybari, Hoda | Advisor(s): Abu-Ghazaleh, Nael | Abstract: Modern computing pl... more Author(s): Naghibijouybari, Hoda | Advisor(s): Abu-Ghazaleh, Nael | Abstract: Modern computing platforms are becoming increasingly heterogeneous, combining a main processor with accelerators/co-processors to perform data-intensive computations. As the most common accelerator, Graphics Processing Units (GPUs) are widely integrated in all computing devices to enhance the performance of both graphics and computational workloads. GPUs as new components in heterogeneous systems introduce potential vulnerabilities and other security problems.This dissertation studies the security of modern GPUs in terms of micro-architectural covert and side channels attacks and defenses. In micro-architectural attacks, information leakage is measured through processes interactions through the shared hardware resources on a processor.The first contribution of my dissertation is a study of covert channel attacks on General Purpose GPUs (GPGPUs). I first reverse engineer the hardware scheduler to create co-...
Machine learning (ML), and deep learning in particular, have become a critical workload as they a... more Machine learning (ML), and deep learning in particular, have become a critical workload as they are becoming increasingly applied at the core of a wide range of application spaces. Computer systems, from the architecture up, have been impacted by ML in two primary directions: (1) ML is an increasingly important computing workload, with new accelerators and systems targeted to support both training and inference at scale; and (2) ML supporting computer system decisions, both during design and run times, with new machine learning based algorithms controlling systems to optimize their performance, reliability and robustness. In this paper, we will explore the intersection of security, ML and computing systems, identifying both security challenges and opportunities. Machine learning systems are vulnerable to new attacks including adversarial attacks crafted to fool a classifier to the attacker's advantage, membership inference attacks attempting to compromise the privacy of the trai...
Proceedings of the ACM International Conference on Supercomputing, 2019
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018
Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture, 2017
IEEE Transactions on Dependable and Secure Computing, 2019
IEEE Computer Architecture Letters, 2017
International Journal of Computer Applications, 2015
IEEE Design & Test, 2021
Editor’s notes: This article demonstrates that architectural side-channel attacks are possible al... more Editor’s notes: This article demonstrates that architectural side-channel attacks are possible also on applications that use the GPUs. —Rosario Cammarota, Intel Labs —Francesco Regazzoni, University of Amsterdam and Università della Svizzera Italiana