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Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi
Tao Wang1, Dandan Yang2, Shunqing Zhang3
1Shanghai Institute for Advanced Communication and Data Science, Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China. twang@shu.edu.cn.
Sensors (Basel, Switzerland)
|May 24, 2019
Summary
This study introduces Wi-Alarm, a WiFi-based intrusion detection system. It efficiently monitors human motion using raw WiFi channel state information (CSI) and a support vector machine (SVM) for accurate detection.
Area of Science:
- Computer Science
- Electrical Engineering
- Cybersecurity
Background:
- Channel State Information (CSI) from WiFi signals can detect human motion.
- Existing methods often require extensive data preprocessing.
Purpose of the Study:
- To develop a WiFi-based intrusion detection system (Wi-Alarm) that bypasses data preprocessing.
- To leverage raw CSI for efficient human motion monitoring.
Main Methods:
- Utilized mean and variance of raw CSI amplitudes for feature extraction.
- Employed a Support Vector Machine (SVM) algorithm for intrusion detection.
- Prototyped and evaluated the system on commercial WiFi devices in an indoor setting.
Main Results:
- Wi-Alarm demonstrated reduced computational expense.
- The system maintained high accuracy and robustness in detection.
- Analysis of various influencing factors was conducted.
Conclusions:
- Raw CSI is a viable and efficient data source for WiFi-based intrusion detection.
- Wi-Alarm offers a computationally inexpensive yet accurate solution for indoor monitoring.
- The system's performance is robust and adaptable to different factors.
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