计算机与数字工程2023，Vol.51Issue(12)：2821-2826,2858,7.DOI:10.3969/j.issn.1672-9722.2023.12.010
ML辅助网络自动化系统的对抗样本攻击方法
An Adversarial Sample Attack for ML-Assisted Network Automation
摘要
Abstract
This paper proposes a black-box adversarial attack scheme to mislead the machine learning(ML)based classifiers for anomaly detection to output incorrect classification results in network automation.Firstly,an adversarial sample generation algo-rithm is designed to synthesize training data for the substitute classifier.The algorithm generates synthetic data to cover all the anom-aly types based on a set of legitimate telemetry data that only containing the"normal"type,and label the synthesized data with mini-mized queries to the legitimate classifier.Then,the generated adversarial sample is leveraged to attack the ML-based classifier to analyze the influence of the adversarial samples on performance of the model,and extend the results to different types of models.Fi-nally,extensive simulations are conducted with the telemetry data collected from a real-world IP over elastic optical network(IP-over-EON)testbed.The results show the effectiveness of the proposed scheme.关键词
网络自动化/黑盒攻击/机器学习/异常检测/对抗样本Key words
network automation(NA)/black-box attack/machine learning(ML)/anomaly detection/adversarial samples分类
信息技术与安全科学引用本文复制引用
潘小琴,尹慧,蔡熠,段康容..ML辅助网络自动化系统的对抗样本攻击方法[J].计算机与数字工程,2023,51(12):2821-2826,2858,7.基金项目
西南科技大学博士基金项目(编号:23zx7123)资助. （编号:23zx7123）
