MUSTER: Subverting User Selection in MU-MIMO Networks
MUSTER: Subverting User Selection in MU-MIMO Networks
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DOI：
10.1109/infocom48880.2022.9796815
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发表时间：
2022-05
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影响因子：
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通讯作者：
Tao Hou;Shen Bi;Tao Wang;Zhuo Lu;Yao-Hong Liu;S. Misra;Y. Sagduyu
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作者：
Tao Hou;Shen Bi;Tao Wang;Zhuo Lu;Yao-Hong Liu;S. Misra;Y. Sagduyu
WiFi 5/6依赖于一个关键特性，多用户多入多出（MU-MIMO），以提供高容量网络吞吐量和频谱效率。MU-MIMO使用基于每个用户信道状态信息（CSI）的用户选择算法来调度一组用户的传输机会，以最大限度地提高服务质量和效率。在本文中，我们发现这种算法为攻击者破坏MU-MIMO中的用户选择创造了一个微妙的攻击面，对当今的无线网络造成了严重的破坏。本文开发了MU-MIMO用户选择策略推理和颠覆（muse）系统，系统地研究了攻击策略，并进一步寻求有效的缓解措施。其设计包括两个主要模块：(i)策略推断，它利用一种名为mc -group的新的神经组学习策略，通过结合循环神经网络（RNN）和蒙特卡罗树搜索（MCTS）来逆向工程用户选择算法，以及（ii）用户选择颠覆，它主动制造CSI来操纵用户选择结果以进行破坏。实验评估表明，该方法对用户选择的预测准确率达到了98.6%左右，能够有效地发动攻击破坏网络性能。最后，我们创建了一种互惠一致性检查技术来防御所提出的攻击，以确保MU-MIMO用户的选择。
WiFi 5/6 relies on a key feature, Multi-User Multiple-In-Multiple-Out (MU-MIMO), to offer high-volume network throughput and spectrum efficiency. MU-MIMO uses a user selection algorithm, based on each user's channel state information (CSI), to schedule transmission opportunities for a group of users to maximize the service quality and efficiency. In this paper, we discover that such algorithm creates a subtle attack surface for attackers to subvert user selection in MU-MIMO, causing severe disruptions in today's wireless networks. We develop a system, named MU-MIMO user selection strategy inference and subversion (MUSTER), to systematically study the attack strategies and further to seek efficient mitigation. MUSTER is designed to include two major modules: (i) strategy inference, which leverages a new neural group-learning strategy named MC-grouping via combining Recurrent Neural Network (RNN) and Monte Carlo Tree Search (MCTS) to reverseengineer a user selection algorithm, and (ii) user selection subversion, which proactively fabricates CSI to manipulate user selection results for disruption. Experimental evaluation shows that MUSTER achieves a high accuracy rate around 98.6% in user selection prediction and effectively launches the attacks to disrupt the network performance. Finally, we create a Reciprocal Consistency Checking technique to defend against the proposed attacks to secure MU-MIMO user selection.