Setareh Rafatirad
University of California, Davis
125 Papers
286 Citations
Setareh Rafatirad is an academic researcher from University of California, Davis. The author has contributed to research in topics: Computer science & Malware. The author has an hindex of 16, co-authored 87 publications. Previous affiliations of Setareh Rafatirad include University of California, Irvine & Islamic Azad University South Tehran Branch.
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Papers
2SMaRT: A Two-Stage Machine Learning-Based Approach for Run-Time Specialized Hardware-Assisted Malware Detection
Hossein Sayadi,Hosein Mohammadi Makrani,Sai Manoj Pudukotai Dinakarrao,Tinoosh Mohsenin,Avesta Sasan,Setareh Rafatirad,Houman Homayoun +6 more
- 25 Mar 2019
TL;DR: This paper identifies the most important HPCs for HMD using an effective feature reduction method and develops a specialized two-stage run-time HMD referred as 2SMaRT, which outperforms state-of-the-art classifiers with 8HPCs by up to 31.25% in terms of detection performance.
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Using Transfer Learning Approach to Implement Convolutional Neural Network model to Recommend Airline Tickets by Using Online Reviews
Maryam Heidari,Setareh Rafatirad +1 more
- 29 Oct 2020
TL;DR: B Bidirectional Encoder Representations from Transformers (BERT) is used for sentiment classification of online reviews to implement a Convolutional Neural Network model that can recommend airline tickets.
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Adversarial Attack on Microarchitectural Events based Malware Detectors
Sai Manoj Pudukotai Dinakarrao,Sairaj Amberkar,Sahil Bhat,Abhijitt Dhavlle,Hossein Sayadi,Avesta Sasan,Houman Homayoun,Setareh Rafatirad +7 more
- 02 Jun 2019
TL;DR: This work creates an adversarial attack on the HMD systems to tamper the security by introducing the perturbations in the HPC traces with the aid of an adversaria sample generator application.
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XPPE: cross-platform performance estimation of hardware accelerators using machine learning
Hosein Mohammadi Makrani,Hossein Sayadi,Tinoosh Mohsenin,Setareh Rafatirad,Avesta Sasan,Houman Homayoun +5 more
- 21 Jan 2019
TL;DR: XPPE, a neural network based cross-platform performance estimation that utilizes the resource utilization of an application on a specific FPGA to estimate the performance on other FPGAs, enables developers to explore the design space without requiring to fully implement and map the application.
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Security and Complexity Analysis of LUT-based Obfuscation: From Blueprint to Reality
Gaurav Kolhe,Hadi Mardani Kamali,Miklesh Naicker,Tyler David Sheaves,Hamid Mahmoodi,P D Sai Manoj,Houman Homayoun,Setareh Rafatirad,Avesta Sasan +8 more
- 01 Nov 2019
TL;DR: This work proposes a pragmatic solution based on a customized LUT, where the security provided by each LUT is superior to that of traditional LUT-based obfuscation and breaks the PPA tradeoffs while exhibiting robustness against the SAT and removal attacks.
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