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Brasser, Ferdinand, Davi, Lucas, Dhavlle, Abhijitt, Frassetto, Tommaso, Dinakarrao, Sai Manoj Pudukotai, Rafatirad, Setareh, Sadeghi, Ahmad-Reza, Sasan, Avesta, Sayadi, Hossein, Zeitouni, Shaza et al..  2018.  Advances and Throwbacks in Hardware-assisted Security: Special Session. Proceedings of the International Conference on Compilers, Architecture and Synthesis for Embedded Systems. :15:1–15:10.
Hardware security architectures and primitives are becoming increasingly important in practice providing trust anchors and trusted execution environment to protect modern software systems. Over the past two decades we have witnessed various hardware security solutions and trends from Trusted Platform Modules (TPM), performance counters for security, ARM's TrustZone, and Physically Unclonable Functions (PUFs), to very recent advances such as Intel's Software Guard Extension (SGX). Unfortunately, these solutions are rarely used by third party developers, make strong trust assumptions (including in manufacturers), are too expensive for small constrained devices, do not easily scale, or suffer from information leakage. Academic research has proposed a variety of solutions, in hardware security architectures, these advancements are rarely deployed in practice.
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Pudukotai Dinakarrao, Sai Manoj, Sayadi, Hossein, Makrani, Hosein Mohammadi, Nowzari, Cameron, Rafatirad, Setareh, Homayoun, Houman.  2019.  Lightweight Node-level Malware Detection and Network-level Malware Confinement in IoT Networks. 2019 Design, Automation Test in Europe Conference Exhibition (DATE). :776–781.
The sheer size of IoT networks being deployed today presents an "attack surface" and poses significant security risks at a scale never before encountered. In other words, a single device/node in a network that becomes infected with malware has the potential to spread malware across the network, eventually ceasing the network functionality. Simply detecting and quarantining the malware in IoT networks does not guarantee to prevent malware propagation. On the other hand, use of traditional control theory for malware confinement is not effective, as most of the existing works do not consider real-time malware control strategies that can be implemented using uncertain infection information of the nodes in the network or have the containment problem decoupled from network performance. In this work, we propose a two-pronged approach, where a runtime malware detector (HaRM) that employs Hardware Performance Counter (HPC) values to detect the malware and benign applications is devised. This information is fed during runtime to a stochastic model predictive controller to confine the malware propagation without hampering the network performance. With the proposed solution, a runtime malware detection accuracy of 92.21% with a runtime of 10ns is achieved, which is an order of magnitude faster than existing malware detection solutions. Synthesizing this output with the model predictive containment strategy lead to achieving an average network throughput of nearly 200% of that of IoT networks without any embedded defense.