Le Gai(盖乐)

Le Gai(盖乐)

Ph.D. Student

Posts by Collection

portfolio

publications

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.
Download Paper

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.

Divide, Predict, Conquer: Adaptive Internet-wide Service Discovery with Limited Seeds

Published in INFOCOM 2026, 2026

To address the challenge of severe reliance on massive prior knowledge (seeds) and low efficiency in Internet-wide service discovery, this project proposes SPADE, an efficient prediction and discovery method featuring adaptivity and divide-and-conquer. This method first acquires a limited number of high-value seeds from target hosts through a multi-level adaptive sampling strategy. Subsequently, drawing on data mining concepts, it progressively extracts two-layer service deployment features using a divide-and-conquer strategy and executes multi-layer predictions in stages. Experiments show that SPADE achieves over 94% Top-1 hit rate and coverage while consuming only 0.15%-1% of the seed volume required by existing state-of-the-art methods. Furthermore, the method accelerates the discovery speed by 219 to 805 times, completing an Internet-wide prediction covering hundreds of millions of hosts within 7 minutes.

Recommended citation: Daguo Cheng, Zedong Jia, Ying Liu, Lin He, Le Gai, et al. "Divide, Predict, Conquer: Adaptive Internet-wide Service Discovery with Limited Seeds." INFOCOM 2026.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.