Abstract

Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.

Highlights

  • Internet of Thing (IoT) technology develops rapidly in recent years

  • An attack method based on deep learning is proposed to select features automatically from

  • The average precision, recall and f1-score of device identification reach over 99%

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Summary

Introduction

Internet of Thing (IoT) technology develops rapidly in recent years. It is reported that the global IoT device market is expected to reach $5.1 billion by 2025 [1]. Wi-Fi device is a major part of IoT devices. More and more researchers begin to pay attention to Wi-Fi security issues with the popularity of Wi-Fi devices. There is a high possibility for emitted radio frequency signals and transmitted frames to be eavedropped by malicious attackers due to the open nature of Wi-Fi. Physical signals and Media Access Control (MAC) frames can be used to identify a device, and the attacker can further track the device trace and analyze device’s vulnerabilities with CVE database [2], which can exploit user’s sensitive information. Distinguishing features which are strongly related to the hardware such as Central Processing

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