IDSDL: a sensitive intrusion detection system based on deep learning

Yanjun Hu,Yafeng Liu,Xuemiao Yang,Fan Bai

doi:10.1186/s13638-021-01900-y

Abstract

Device-free passive (DfP) intrusion detection system is a system that can detect moving entities without attaching any device to the entities. To achieve good performance, the existing algorithms require proper access point (AP) deployment. It limits the applying scenario of those algorithms. We propose an intrusion detection system based on deep learning (IDSDL) with finer-grained channel state information (CSI) to free the AP position. A CSI phase propagation components decomposition algorithm is applied to obtain blurred components of CSI phase on several paths as a more sensitive detection signal. Convolutional neuron network (CNN) of deep learning is used to enable the computer to learn and detect intrusion without extracting numerical features. We prototype IDSDL to verify its performance and the experimental results indicate that IDSDL is effective and reliable.

Highlights

Passive intrusion detection technique has become one of the current research hotspots due to its characteristic of detecting entities without carrying any device
The Convolutional neuron network (CNN) classifier is trained in the offline phase and new channel state information (CSI) data is detected in the online phase
We utilize a path decomposition algorithm and CNN to improve the sensitivity of passive intrusion detection system

Summary

Introduction

Passive intrusion detection technique has become one of the current research hotspots due to its characteristic of detecting entities without carrying any device. WiFi-based passive intrusion detection is a system using signals which can be affected by human motion [6]: received signal strength (RSS) and channel state information (CSI). Previous passive detection algorithms extract features such as mean and variance [11, 12] from RSS and CSI data to accomplish the function.

Results

Conclusion