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Hidden Backdoor Attack Against Deep Learning-Based Wireless Signal Modulation Classifiers
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AI
DOI：10.1109/TVT.2023.3267455.png)
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摘要
En 中文
Recently, DL has been exploited in wireless communications such as modulation classification. However, due to the openness of wireless channel and unexplainability of DL, it is also vulnerable to adversarial attacks. In this correspondence, we investigate a so called hidden backdoor attack to modulation classification, where the adversary puts elaborately designed poisoned samples on the basis of IQ sequences into training dataset. These poisoned samples are hidden because it could not be found by traditional classification methods. And poisoned samples are same to samples with triggers which are patched samples in feature space. We show that the hidden backdoor attack can reduce the accuracy of modulation classification significantly with patched samples. At last, we propose activation cluster to detect abnormal samples in training dataset.
Keyword:
Deep learning
modulation classification
communications security
backdoor attack
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
Deep Learning-Based Signal Modulation Identification in OFDM Systems基于深度学习的OFDM系统信号调制识别.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing使用深度学习的调制分类综述: 信号表示和数据预处理
PREAI
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