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DTSTART;TZID=America/New_York:20210622T130000
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UID:26318-1624366800-1624370400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Majid Sabbagh
DESCRIPTION:PhD Dissertation Defense: The Perils of Shared Computing: A Hardware Security Perspective \nMajid Sabbagh \nLocation: Microsoft Teams Link \nAbstract: Modern processors and hardware accelerators\, in the cloud or on the edge\, are capable of running multiple workloads from different users concurrently. Despite software techniques for security such as virtualization and containers\, a new attack surface is emerging that pertains to the hardware vulnerabilities of shared computing resources\, posing serious threats to shared computing. Fault attacks (FAs)\, Side-Channel Attacks (SCAs)\, and Transient-Execution Attacks (TEA) are three hardware-oriented attacks that target the system implementations. FAs aim to tamper the integrity of application execution through different fault injection methods\, to compromise the data or disrupt computation at run-time. SCAs exploit the information leakage of sensitive applications in physical parameters\, such as power consumption\, electromagnetic emanations\, and timing\, to breach the confidentiality of the application. TEAs exploit transient hardware operations such as speculative execution in Central Processing Units (CPUs) to tap on sensitive data temporarily and retrieve them from implicative microarchitectural states.\nIn this dissertation\, we investigate the three kinds of attacks that all exploit vulnerabilities due to shared computing. We first introduce a new non-invasive FA against Graphics Processing Units (GPUs)\, called overdrive fault attacks. We discover the security vulnerability of GPU’s voltage-frequency scaling (VFS) mechanism\, a common feature to balance power consumption and performance. An out-of-specification configuration of GPU voltage and frequency can be set by an adversary on the host CPU\, through the software interfaces to GPU’s power management units. This setting will cause timing violations for the computation and result in silent data corruptions (SDCs). We apply the overdrive fault attacks on two common victim applications. One is cryptographic applications accelerated by GPU. We launch a differential fault analysis (DFA) attack on an AES kernel running on an AMD RX 580 GPU and successfully recover the secret key. The other victim is convolutional neural network (CNN) inference. We thoroughly characterize fault injections and propagation in a CNN on a GPU and analyze the controllability of the attack. We successfully launch an end-to-end misclassification attack during CNN inferences with careful timing control.\nWe then evaluate a timing side-channel attack called Prime+Probe attack on CPUs and propose a Side-Channel Attack DEtection Tool (SCADET). SCADET is a methodology and a tool that operates on an x86 program’s binary. It records and analyzes the program’s memory accesses using dynamic binary instrumentation by running the program in a controlled environment to accurately identify the malicious access patterns demonstrated by the Prime+Probe attack.\nFinally\, we introduce an efficient hardware-level taint-tracking defense against the most prominent TEAs\, the speculative execution attacks. We take a secure-by-design approach and propose a mechanism called Secure Speculative Execution via RISC-V Open Hardware Design (SSE-RV)\, based on the latest Berkeley Out-of-Order Machine (SonicBOOM). We prototype our SSE-RV processor on an FPGA running a Linux operating system. Our results show that we can protect against Spectre-v1\, v2\, and v5. Our defense scheme is general and can be extended to protect against other transient execution attacks.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-majid-sabbagh/
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DTSTART;TZID=America/New_York:20210622T140000
DTEND;TZID=America/New_York:20210622T150000
DTSTAMP:20260512T044516
CREATED:20210614T171638Z
LAST-MODIFIED:20210614T171638Z
UID:26295-1624370400-1624374000@coe.northeastern.edu
SUMMARY:PhD Dissertation Defense: Ala Tokhmpash
DESCRIPTION:PhD Dissertation Defense: Fractional Order Derivative in Circuits\, Systems\, and Signal Processing with Specific Application to Seizure Detection \nAla Tokhmpash \nLocation: Zoom Link \nAbstract: Epilepsy is a chronic brain disease that affects around 50 million people worldwide. This disease is characterized by recurrent seizures\, which are brief episodes of involuntary movement that may involve a part or the entire body and are sometimes accompanied by loss of consciousness. It is the third most common neurological disorder in the United States\, only after Alzheimer’s disease and stroke. Patients suffering from epilepsy\, a brain disorder\, can have more than one type of seizure. Seizure detection systems can be life-changing for patients with epileptic seizures. By accurately identifying the periods in which seizure occurrence has a higher chance of happening we can help epileptic patients live a more normal life. Prior works on automated seizure detection overwhelmingly either rely solely entirely on domain knowledge\, or instead use a black box deep learning model. This thesis aims to integrate machine learning techniques with available seizure detection methods to improve detection performance. In this process\, we take advantage of mathematical tools provided by fractional-order derivatives as well as fuzzy entropy concepts. Specifically\, 1) we show the effectiveness of fractional order methods (FOM) in representing signals with long-range dependencies 2) using case studies in control and power systems\, we further examine the performance of FOM in the presence of parameter uncertainty. 3) using two publicly available data sets of brain signals from multiple patients\, we develop a cohesive framework to leverage FOM for extracting features that can be then used by statistical learning methods. 4) following recent works in this field\, we generalize the notion of entropy to include the fractional-order case. Combined with the fuzzy sets describing the uncertainty in data\, we leverage fractional fuzzy entropy as a robust descriptor of the state of brain signals. Through these case studies\, we demonstrate a significant increase in performance accuracy compared to models that do not consider FOM.
URL:https://coe.northeastern.edu/event/phd-dissertation-defense-ala-tokhmpash/
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