StinAttack:一种针对星地集成网络集成入侵防御系统的轻量级有效对抗攻击仿真

Shangyuan Zhuang, Jiyan Sun, Hangsheng Zhang, Xiaohui Kuang, Ling Pang, Haitao Liu, Yinlong Liu
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引用次数: 0

摘要

有效的对抗性攻击仿真是星地集成网络中集成入侵检测系统部署的关键。这是因为它可以自动生成大量的对抗样本来评估不同分类器的鲁棒性。在此基础上,进一步指导STIN工程师在集成ids中选择合适的分类器。此外,利用入侵防御系统在对抗攻击过程中的自学习特性,可以帮助入侵防御系统提高检测性能。然而,由于STIN的计算资源有限,通信链路较长,现有的对抗性攻击方法存在着成功率低、通信和计算开销大的问题。这导致它们在STIN中的效率低下。为了解决上述问题,我们提出了StinAttack作为STIN的鲁棒性评估方案。首先,StinAttack为地面和卫星节点之间的交互次数很少的STIN中入侵防御系统提供了一个全面、自动的鲁棒性评估框架。其次,StinAttack针对集成入侵防御系统提出了一种有效的基于轻量级梯度评估的对抗攻击仿真方法。第三,我们对11种典型的ids, 4种基线流行的对抗性攻击和我们的StinAttack进行了实验。实验结果表明,该方法可以有效地攻击集成入侵攻击,基于真实STIN数据集的评估结果对安全网络的设计具有指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
StinAttack: A Lightweight and Effective Adversarial Attack Simulation to Ensemble IDSs for Satellite- Terrestrial Integrated Network
Effective adversarial attacks simulation is essential for the deployment of ensemble Intrusion Detection Systems (en- semble IDSs) in Satellite-Terrestrial Integrated Network (STIN). This is because it can automatically generate a large amount of adversarial samples to evaluate the robustness of different classifiers. Based on the result, it can further guide the STIN engineers to select proper classifiers in ensemble IDSs. Moreover, it can help the IDSs improve detect performance by their self- learning property in the adversarial attack process. However, the existing adversarial attack approaches suffer from the problems of low success rate and high overhead of communication and calculation due to the limited computing resources and long communication links of STIN. This results in their inefficiency in STIN. To address the above problems, we provide StinAttack as a robustness evaluation scheme for STIN. First, StinAttack provides a comprehensive and automatic robustness evaluation framework for IDSs in STIN with only few times interactions between terrestrial and satellite nodes. Second, StinAttack proposes an effective adversarial attack simulation based on lightweight gradient evaluation for ensemble IDSs. Third, we conduct experiments on 11 typical IDSs, 4 baseline popular adversarial attacks and our StinAttack. Experimental results show that our approach can effectively attack ensemble IDSs and the evaluation results based on real STIN dataset are instructive for designing secure networks.
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