Towards Neural Network-Based Communication System: Attack and Defense
Towards Neural Network-Based Communication System: Attack and Defense
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DOI：
10.1109/tdsc.2022.3203965
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发表时间：
2023-07
影响因子：
7.3
通讯作者：
Zuobin Xiong;Zhipeng Cai;Chun-qiang Hu;Daniel Takabi;Wei Li
中科院分区：
文献类型：
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作者：
Zuobin Xiong;Zhipeng Cai;Chun-qiang Hu;Daniel Takabi;Wei Li
最近的进步见证了神经网络在许多新兴应用中的出色成功，例如图像识别，文本分类和语音分析。为了实现安全的沟通，已经实现了神经网络的利用，但尚未引起足够的研究关注。此外，由于其关键的安全缺陷，现有的基于神经网络的通信系统缺乏。在本文中，我们研究了现有神经通信系统的安全漏洞。基于我们的分析，我们设计了两种攻击模型，包括目标人物攻击和目标欺诈攻击。之后，为了提高神经通信系统的安全性能，我们开发了一种新的防御机制，以通过将秘密密钥与明文分开并将防御性损失纳入训练过程来促进双向安全通信。此外，我们通过理论证明展示了我们提出的神经通信系统的有效性。最后，我们实施了全面的真实数据实验，以评估分类准确性，沟通效率和沟通符合条件的方面的攻击和防御方法的性能，这证实了我们所提出的神经通信系统的优势艺术。
Recent progress has witnessed the excellent success of neural networks in many emerging applications, such as image recognition, text classification, and speech analysis. In order to achieve secure communication, the utilization of neural networks has been realized yet has not raised sufficient research attention. In addition, the existing neural network-based communication system falls short due to its critical security flaws. In this article, we investigate the security vulnerabilities of the existing neural communication system. Based on our analysis, we design two kinds of attack models, including target man-in-the-middle attack and target fraud attack. After that, to improve the security performance of neural communication systems, we develop a new defense mechanism to facilitate two-way secure communication by separating secret key from plaintext and incorporating defensive loss into the training process. Moreover, we show the effectiveness of our proposed neural communication system via theoretical proof. Finally, we implement comprehensive real data experiments to evaluate the performance of our attack and defense methods from the aspects of classification accuracy, communication efficiency and communication qualify, which confirms the advantages of our proposed neural communication system compared with the state-of-the-art.