RAGN: Detecting unknown malicious network traffic using a robust adaptive graph neural network

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Computer Networks Pub Date : 2025-03-10 DOI:10.1016/j.comnet.2025.111184
Ernest Akpaku , Jinfu Chen , Mukhtar Ahmed , Francis Kwadzo Agbenyegah , William Leslie Brown-Acquaye
{"title":"RAGN: Detecting unknown malicious network traffic using a robust adaptive graph neural network","authors":"Ernest Akpaku ,&nbsp;Jinfu Chen ,&nbsp;Mukhtar Ahmed ,&nbsp;Francis Kwadzo Agbenyegah ,&nbsp;William Leslie Brown-Acquaye","doi":"10.1016/j.comnet.2025.111184","DOIUrl":null,"url":null,"abstract":"<div><div>As network environments evolve, detecting unknown malicious network traffic becomes increasingly challenging due to the dynamic and sophisticated nature of modern cyberattacks. Graph Attention Networks (GATs) have shown promise in modeling complex network interactions but remain vulnerable to adversarial attacks that exploit weaknesses in the graph structure. In this work, we propose the Robust Adaptive Graph Neural Network (RAGN), an enhanced GAT-based framework that introduces adaptive attention mechanisms to improve detection accuracy and robustness against adversarial manipulations in network traffic graphs. RAGN iteratively adjusts the graph structure and feature space to suppress adversarial perturbations by assigning lower attention scores to unreliable edges and refining feature representations based on the feature smoothness regularization principle. To assess the robustness of the proposed RAGN model and compare it with baseline models, we introduced an effective dynamic graph attack method known as Semantic-Preserving Adversarial Node Injection (SPAN). We benchmarked its performance against state-of-the-art graph attack methods, including DICE, DGA, and RWCS. SPAN incrementally injects small batches of malicious nodes, refining their edges and features to target both the structural and temporal aspects of dynamic graphs. It preserves semantic integrity, and generates effective yet imperceptible perturbations, providing a rigorous test of the resilience of graph neural networks. Experiments conducted on four datasets, demonstrate that RAGN demonstrates robustness against adversarial, and zero-day attacks. It also demonstrates resilience against targeted, malicious node injection attacks in dynamic network environments. RAGN demonstrated consistent robustness, with misclassification rates increasing only marginally (by less than 1.2%) even under significant dynamic changes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111184"},"PeriodicalIF":4.4000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389128625001525","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

Abstract

As network environments evolve, detecting unknown malicious network traffic becomes increasingly challenging due to the dynamic and sophisticated nature of modern cyberattacks. Graph Attention Networks (GATs) have shown promise in modeling complex network interactions but remain vulnerable to adversarial attacks that exploit weaknesses in the graph structure. In this work, we propose the Robust Adaptive Graph Neural Network (RAGN), an enhanced GAT-based framework that introduces adaptive attention mechanisms to improve detection accuracy and robustness against adversarial manipulations in network traffic graphs. RAGN iteratively adjusts the graph structure and feature space to suppress adversarial perturbations by assigning lower attention scores to unreliable edges and refining feature representations based on the feature smoothness regularization principle. To assess the robustness of the proposed RAGN model and compare it with baseline models, we introduced an effective dynamic graph attack method known as Semantic-Preserving Adversarial Node Injection (SPAN). We benchmarked its performance against state-of-the-art graph attack methods, including DICE, DGA, and RWCS. SPAN incrementally injects small batches of malicious nodes, refining their edges and features to target both the structural and temporal aspects of dynamic graphs. It preserves semantic integrity, and generates effective yet imperceptible perturbations, providing a rigorous test of the resilience of graph neural networks. Experiments conducted on four datasets, demonstrate that RAGN demonstrates robustness against adversarial, and zero-day attacks. It also demonstrates resilience against targeted, malicious node injection attacks in dynamic network environments. RAGN demonstrated consistent robustness, with misclassification rates increasing only marginally (by less than 1.2%) even under significant dynamic changes.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
期刊最新文献
Deep Reinforcement Learning and SQP-driven task offloading decisions in vehicular edge computing networks Decentralized traffic detection utilizing blockchain-federated learning with quality-driven aggregation RAGN: Detecting unknown malicious network traffic using a robust adaptive graph neural network Editorial Board Social network botnet attack mitigation model for cloud
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1