Detection of Malicious FPGA Bitstreams using CNN-Based Learning

Author:

Chaudhuri Jayeeta1,Chakrabarty Krishnendu1

Affiliation:

1. Duke University,Department of Electrical and Computer Engineering,Durham,NC

Funder

National Science Foundation

Publisher

IEEE

Reference12 articles.

1. An inside job: Remote power analysis attacks on FPGAs

2. FPGA-Based Remote Power Side-Channel Attacks

3. A semantic matching energy function for learning with multi-relational data;glorot;Machine Learning,2013

4. IP Core Identification in FPGA Configuration Files using Machine Learning Techniques

5. Learning data augmentation strategies for object detection;zoph;CoRR,2019

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1. Detection of Stealthy Bitstreams in Cloud FPGAs using Graph Convolutional Networks*;2024 IEEE European Test Symposium (ETS);2024-05-20

2. MaliGNNoma: GNN-Based Malicious Circuit Classifier for Secure Cloud FPGAs;2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST);2024-05-06

3. Diagnosis of Malicious Bitstreams in Cloud Computing FPGAs;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-11

4. A Covert Attack Method Against FPGA Clouds;2023 IEEE 32nd Asian Test Symposium (ATS);2023-10-14

5. FPGA-Patch: Mitigating Remote Side-Channel Attacks on FPGAs using Dynamic Patch Generation;2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED);2023-08-07

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