计算机科学
无线
软件部署
变压器
无线网络
网络数据包
人工智能
实时计算
计算机网络
工程类
电信
电压
电气工程
操作系统
作者
Zhixin Shi,Hao Wu,Jing Zhang,Meng Zhang,Weiqing Huang
标识
DOI:10.1109/iscc58397.2023.10218088
摘要
The wireless cameras have become a major concern in cybersecurity due to privacy breaches. Recent flow based methods for wireless cameras detection have achieved promising results. However, these methods require specialized equipment for deployment and massive labeled data for training, which makes them impractical in real-world scenarios. In this paper, we propose FindSpy, a lightweight wireless camera detection method based on Pre-trained Transformers to address the challenge. By utilizing the air interface technique, FindSpy can obtain data without connecting to the wireless network where the camera is located. Additionally, FindSpy learns air interface WiFi traffic representation by pre-training a traffic representation model from large-scale unlabeled data and fine-tuning it on few labeled data. FindSpy can accurately detect wireless cameras with CNN-LSTM classifier. Extensive experiments show that FindSpy outperforms the state-of-the-art methods on few data. Concretely, FindSpy achieves a detection accuracy of over 98% by analyzing just five data packets.
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