CNN-Based Attack Defense for Device-Free Localization

• Main Authors: Han, Z. (Author), Huang, H. (Author), Lian, Z. (Author), Lin, L. (Author), Qiu, C. (Author), Su, C. (Author), Wang, Z. (Author), Zhao, L. (Author)
• Format: Article
• Language: English
• Published: Hindawi Limited 2022
• Subjects: Convolutional neural network; Convolutional neural networks; Device-free localizations; Environment models; Intrusion detection; Intrusion-Detection; Localization technologies; Network security; Network-based attacks; Signal receivers; Signal strengths; Strength differences; Strength values; Transmitter and receiver
• Online Access: View Fulltext in Publisher

 Device-free localization technology aims to find a target by analyzing the signal strength difference between transmitter and receiver deployed in the target area in advance. Up to now, device-free localization technology has been applied to a wide range of applications and scenarios, such as intrusion detection, environment modeling, and activity recognition. However, some sensors remain at potential risk that signal strength values of sensors have been tampered, or even devices sensors are physically damaged, which leads to inaccurate location results or a whole system crash. To solve the abovementioned problems, we design a CNN-based attack defense method for device-free localization, which can discover falsified signal strength values and error-prone devices. Firstly, we simulate a partial sensor attack or dropout in the device-free localization scenario. Then, we transform the localization problem into an image classification problem and use the convolutional neural networks (CNN) technique for abnormal detection. The experiment result shows that our algorithm can maintain high localization accuracy even under most sensor compromised and disconnected circumstances. © 2022 Zhaoyang Han et al.