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X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;TZID=America/New_York:20230602T110000
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DTSTAMP:20260512T092101
CREATED:20230508T153647Z
LAST-MODIFIED:20230508T153647Z
UID:36931-1685703600-1685707200@coe.northeastern.edu
SUMMARY:Cheng Gongye's PhD Proposal Review
DESCRIPTION:“Hardware Security Vulnerabilities in Deep Neural Networks and Mitigations” \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Xue Lin\nProf. Xiaolin Xu \nAbstract:\nOver the past decade\, Deep Neural Networks (DNNs) have revolutionized numerous fields. With the increasing deployment of DNN models in security-sensitive and mission-critical applications\, such as autonomous driving\, ensuring the security and privacy of DNN inference is of paramount importance. \nThis Ph.D. dissertation investigates two primary hardware security attack vectors: fault attacks and side-channel attacks. Fault attacks compromise the integrity of a targeted application by intentionally disrupting the computation or injecting faults on parameters. Side-channel attacks exploit information leakage from the application execution through physical parameters such as power consumption\, electromagnetic emanations\, and timing to retrieve secrets\, thereby breaching confidentiality. \nFor fault attacks\, we demonstrate a power-glitching fault injection attack on FPGA-based DNN accelerators in cloud environments. The attack exploits vulnerabilities in the shared power distribution network and leverages time-to-digital converter (TDC) sensors for precise fault injection timing\, and results in model misclassification\, an integrity compromise on the targeted application. We propose a lightweight defense framework for detecting and mitigating adversarial bit-flip attacks induced by RowHammer on DNNs. This framework employs a dynamic channel-shuffling obfuscation scheme and a logits-based model integrity monitor\, offering negligible performance loss. This framework effectively protects various DNN models from RowHammer attacks without any retraining or model structure modifications. \nFor side-channel attacks\, we present a floating-point timing side channels attack to reverse-engineer multi-layer perceptron (MLP) model parameters in software implementations. This attack successfully recovers DNN parameters\, weights and biases. \nRegarding ongoing research\, we observe that previous studies often focus on academic prototypes\, resulting in limited applicability. To bridge these gaps\, we select the AMD-Xilinx DPU\, one of the most advanced DNN accelerators to date\, to conduct the analysis. We propose a side-channel attack that utilizes electromagnetic emissions to extract parameters. Furthermore\, we propose a comprehensive fault analysis of quantized DNN models by simulations and discuss potential mitigation strategies.
URL:https://coe.northeastern.edu/event/cheng-gongyes-phd-proposal-review/
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LAST-MODIFIED:20230601T171608Z
UID:37168-1685703600-1685707200@coe.northeastern.edu
SUMMARY:MIE Seminar - Dr. Andrew Akbashev
DESCRIPTION:Please join us for a special seminar with Dr. Andrew Akbashev\, visiting from the Paul Scherrer Institute (Switzerland) to give a talk titled “Electrochemistry: Fundamental Research\, Academic Culture and Education”. \nHe is a leading expert in electrochemistry and catalysis research and has a large following on social media (>40\,000 followers on LinkedIn) discussing various issues in academia\, education\, and frequently shares valuable career advice for under/graduate students and early career professionals. As an example\, he has discussed “Tackling overpublishing by moving to open-ended papers” in Nature Materials. Further\, he is the founder and solo organizer of the Electrochemical Online Colloquium frequently attracting an audience of up to 700 attendees. \nPlease mark your calendars for this treat – the seminar will take place Friday 6/2\, from 11:00 am to 12:00 pm in 168 Snell Engineering and via Zoom. Light snacks will be served.
URL:https://coe.northeastern.edu/event/mie-seminar-dr-andrew-akbashev/
LOCATION:168 SN\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
CATEGORIES:use the department, audience, and topic lists
ORGANIZER;CN="Mechanical & Industrial Engineering":MAILTO:mie-web@coe.neu.edu
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