# Intrusion Detection Based on Device-Free Localization in the Era of IoT

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## Abstract

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## 1. Introduction

- We devise the RSS signal as the image matrix and then transform the DFL problem into the image classification problem.
- We propose a scheme of BE-CNN for the outdoor localization scenario, extracting features from the RSS-image automatically.
- The performance of the proposed BE-CNN scheme is validated on real-world datasets of outdoor DFL and compared with other baseline and state-of-the-art DFL methods.

## 2. Related Work

## 3. Problem Statement

#### 3.1. Description of the Device-Free Localization Problem

#### 3.2. Transformation of DFL the Problem into the Image-Classification Problem

#### 3.3. Dataset Construction

## 4. Proposed Approach

## 5. Performance Evaluation

#### 5.1. Configurations of the Experiment

#### 5.2. Data Pre-Processing

#### 5.3. Localization Performance of the BE-CNN Scheme for the Outdoor DFL

#### 5.3.1. Optimal Parameters of the BE-CNN

#### 5.3.2. Localization Performance Comparison of the BE-CNN Scheme

#### 5.4. Discussion on the Drawbacks and Future Work

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Internet-of-Things (IoT) fundamental blocks with device-free localization (DFL) technique for the indoor scenario of intrusion detection and monitoring in a smart city.

**Figure 2.**Illustration of the framework of the proposed background elimination pre-processing based convolutional neural network (BE-CNN).

**Figure 6.**Experimental layout of the outdoor DFL scenario. The reference-point (RP) IDs of the top row are from RP-31–RP-36; the RPs of the second row are from RP-25–RP-30; RPs of the third row are from RP-19–RP-24; RPs of the forth row are from RP-13–RP-18; RPs of the fifth row are from RP-7–RP-12; RPs of the bottom row are from RP-1–RP-6.

**Figure 7.**Comparisons of the raw RSS matrices (top row) and background eliminated RSS matrices (bottom row). Here, the data of Reference Position 2 (RP2) and RP4 are taken as the examples.

**Figure 8.**Featured images of the noiseless signal and noisy signal with different SNR. Here, RP4 is taken as the example.

**Figure 10.**Comparisons of localization accuracy by employing different numbers of sensors. Here, the distance between the adjacent sensors is 3 feet, 9 feet, and 12 feet for sensor numbers of 28, 8, and 7, respectively.

**Table 1.**Training the simulation parameters with different kernel sizes in terms of localization accuracy.

SNR | Kernel Size | ||
---|---|---|---|

$3\times 3\u20137\times 7$ | $3\times 3\u20139\times 9$ | $3\times 3\u201311\times 11$ | |

−5 dB | 99.9% | 100% | 99.9% |

−10 dB | 82.5% | 84.9% | 82.9% |

Number of Convolutional Layers | With Pooling Layer | Without Pooling Layer |
---|---|---|

1 layer | 94.9% | 99.8% |

2 layers | 96.2% | 100% |

3 layers | 64.6% | 91.6% |

Key Parameters | Optimization |
---|---|

Convolutional layer number | 2 |

Concatenated convolutional filter size | 9 × 9, 3 × 3 |

Filter number for each layer | 32 |

Subsampling operation | Without pooling |

Epoch number | 100 |

Learning rate | ${10}^{-4}$ |

Batch size | 300 |

Dropout rate | 0.4 |

**Table 4.**Localization accuracy compared with other methods. SC-ISTA, sparse coding method based on the iterative shrinkage-thresholding algorithm.

BE-CNN | BE-SVM | BE-KNN | BE-AE | SC-ISTA | SRC-CVX | |
---|---|---|---|---|---|---|

Localization Accuracy | 100% | 88.9% | 50.6% | 97.2% | 2.8% | 2.8% |

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## Share and Cite

**MDPI and ACS Style**

Zhao, L.; Su, C.; Huang, H.; Han, Z.; Ding, S.; Li, X.
Intrusion Detection Based on Device-Free Localization in the Era of IoT. *Symmetry* **2019**, *11*, 630.
https://doi.org/10.3390/sym11050630

**AMA Style**

Zhao L, Su C, Huang H, Han Z, Ding S, Li X.
Intrusion Detection Based on Device-Free Localization in the Era of IoT. *Symmetry*. 2019; 11(5):630.
https://doi.org/10.3390/sym11050630

**Chicago/Turabian Style**

Zhao, Lingjun, Chunhua Su, Huakun Huang, Zhaoyang Han, Shuxue Ding, and Xiang Li.
2019. "Intrusion Detection Based on Device-Free Localization in the Era of IoT" *Symmetry* 11, no. 5: 630.
https://doi.org/10.3390/sym11050630