文献类型：会议论文

英文题名：Botnet Detection Based on Multilateral Attribute Graph

作者：Cheng, Hua[1];Shen, Yinda[1];Cheng, Tao[1];Fang, Yiquan[2];Ling, Jianfan[1]

机构：[1]East China Univ Sci & Technol, Coll Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Informat Off, Shanghai 200237, Peoples R China

会议论文集：3rd Annual International Conference on Science of Cyber Security (SciSec)

会议日期：AUG 13-15, 2021

会议地点：Fudan Univ, ELECTR NETWORK

主办单位：Fudan Univ

语种：英文

外文关键词：Botnet; Latent Graphic Convolutional Neural Network (Latent-GCN); Multilateral attribute graph

摘要：Botnets have become the infrastructure of cryptocurrency in recent years, but traditional graph-based detection methods ignore multiple flows and their features. We propose a botnet detection method (ME-LGCN) by node classification based on the fine-grained multilateral attribute graph (fMAG). Multiple flows and their features are appended on the simple graph of network topology as multilateral structures and attributes in fMAG. Latent Graph Convolutional Neural Network (Latent-GCN) is used for node classification, where multi-edge embedding learns the multilateral attributes as an interaction vector, direct on-vertex embedding extends node representation, and GCN aggregates information of neighborhoods. Experiments on real datasets show that ME-LGCN provides significant improvements compared to other methods with a more than 3% improvement in F1.