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DawnGNN: Documentation augmented Windows malware detection using graph neural network

Published in Computers & Security, 2024

We introduce DawnGNN, a novel Windows malware detection framework leveraging official API documentation and graph neural networks. It converts API sequences into graphs, encodes API descriptions using BERT, and employs a Graph Attention Network for detection. Tested on three datasets, DawnGNN demonstrates enhanced detection capabilities, showcasing the value of API documentation in malware analysis.

Recommended citation: Pengbin Feng, Le Gai, Li Yang, Qin Wang, Teng Li, Ning Xi, Jianfeng Ma. " DawnGNN: Documentation augmented Windows malware detection using graph neural network." Computers & Security. 2024: 103788.
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Fast Calculation of National Commercial Cryptographic Algorithm Based on RISC-V Processing Core

Published in ChinaSoft 2024, 2024

The project implements and optimizes the national cryptographic algorithms on a domestic RISC-V platform, achieving both software and hardware acceleration. It optimizes the critical computational steps of bilinear pairing and elliptic curves at the instruction level, designs cryptographic processing units for point multiplication and modular exponentiation respectively, and ultimately accelerates the computation of the national cryptographic algorithms.

Recommended citation: Ning Zhang, Le Gai, Pengbin Feng. "Fast Calculation of National Commercial Cryptographic Algorithm Based on RISC-V Processing Core." ChinaSoft 2024.

Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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