文献类型：期刊文献

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] College of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Information Office, East China University of Science and Technology, Shanghai, 200237, China

年份：2021

卷号：13005 LNCS

起止页码：66

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20214411093219)

语种：英文

外文关键词：Embeddings - Graph neural networks - Classification (of information) - Botnet - Convolution - Convolutional neural networks - Graphic methods

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. ? 2021, Springer Nature Switzerland AG.