通信学报2024，Vol.45Issue(6)：75-86,12.DOI:10.11959/j.issn.1000-436x.2024093
新增未知攻击场景下的工业互联网恶意流量识别方法
Identification method for malicious traffic in industrial Internet under new unknown attack scenarios
摘要
Abstract
Aiming at the problem of traffic data distribution shift caused by new unknown attacks in the industrial Inter-net,a malicious traffic identification method based on neighborhood filtering and stable learning was proposed to en-hance the effectiveness and robustness of the existing graph neural network model in identifying known malicious traffic.Firstly,the graph structure of the traffic data was modeled to capture the topological relationship and interaction mode in communication behavior.Secondly,the traffic subgraph was divided based on the neighborhood filtering mechanism of biased sampling to eliminate the pseudo-homogeneity between communication behaviors.Finally,the statistical indepen-dence of high-dimensional traffic features was realized by applying graph representation learning and stable learning strategies,combined with adaptive sample weighting and collaborative loss optimization methods.The experimental re-sults on two benchmark datasets show that compared with the baseline method,the recognition performance of the pro-posed method is increased by more than 2.7%in the new unknown attack scenario,which shows its high efficiency and practicability in the industrial Internet environment.关键词
工业互联网/恶意流量识别/图神经网络/邻域过滤/稳定学习Key words
industrial Internet/malicious traffic identification/graph neural network/neighborhood filtering/stable learning分类
计算机与自动化引用本文复制引用
曾凡一,苘大鹏,许晨,韩帅,王焕然,周雪,李欣纯,杨武..新增未知攻击场景下的工业互联网恶意流量识别方法[J].通信学报,2024,45(6):75-86,12.基金项目
国家重点研发计划基金资助项目(No.2021YFB3101403) （No.2021YFB3101403）
国家自然科学基金资助项目(No.U2003206,No.U20B2048,No.U21B2019,No.U22A2036,No.62272127) （No.U2003206,No.U20B2048,No.U21B2019,No.U22A2036,No.62272127）
黑龙江省自然科学基金资助项目(No.TD2022F001)The National Key Research and Development Program of China(No.2021YFB3101403),The National Natural Science Foundation of China(No.U2003206,No.U20B2048,No.U21B2019,No.U22A2036,No.62272127),The Natural Science Foundation of Heilongjiang Province(No.TD2022F001) （No.TD2022F001）
