1. The document proposes using autoencoder neural networks for anomaly detection in wireless sensor networks (WSN) in an Internet of Things (IoT) system. 2. It has minimal communication and computation requirements compared to other methods, and the distributed nature is well-suited for WSN. 3. The paper presents a two-part algorithm using a single hidden layer autoencoder model and evaluates its performance on temperature and humidity sensor data from a WSN testbed, showing it can accurately detect anomalies over 90% of the time.

1. Distributed Anomaly Detection using
Autoencoder Neural Networks in WSN for IoT
SPEAKER: Shun-Yu, Ko
ID:107598068

2. Outline
1. Introduction
2. Anomaly detection
3. Autoencoder neural network
4. System architecture and two-part algorithm
5. Performance evaluation
6. Results
7. Conclusion
8. Reference

3. Introduction

4. Introduction

5. Introduction
1. In this paper, we propose to use autoencoder neural networks
for anomaly detection in WSN.
2. It has the minimal communication and computation requirements
and the distributed advantage.
3. This work is the first to introduce Autoencoder into WSN.
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6. Autoencoder neural network
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7. Autoencoder neural network
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8. The hyper parameters
• Neural Network=input+Weight+Bias+Activation function
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9. System Architecture
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10. WSN Computational complexity
• MAX Dimension : M ( Ex : 720 )
• Computation time : O(M2)
According to the paper : Scalable hypergrid
k-Nnbased online anomaly detection in wireless
sensor networks , IEEE Transactions on Parallel and
Distributed Systems, vol. 24, no. 8, pp. 1661–1670,
2013 ,it cost O(2M-1) Computation time !
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11. PERFORMANCE EVALUATION
WSN Testbed and Dataset
1. 8 sensor nodes that monitor temperature and relative humidity.
2. 720 daily readings from a single sensor
3. Run for 4 months.
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12. ROC Curve(receiver operating characteristic curve)
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有高血壓 無高血壓

13. ROC Curve
• Accuracy = (TP + TN) / (P + N)
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14. Area under the Curve of ROC (AUC ROC)）
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15. Results- Varying anomaly magnitude:
1. Vary the magnitude v of spikes and
bursts .
2. Fixing the frequency of anomalies at K =
100 per day.
3. AUC > 0.8 most of the time, which
indicates a good classifier.
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16. Results- Varying anomaly frequency:
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17. CONCLUSION
1. This paper presents the first effort of introducing autoencoder
neural networks into WSN to perform anomaly detection.
2. Communication overhead is minimized and computational load is
allocated to the most suitable entities.
3. Single hidden layer of neurons and the corresponding
computational complexity is only polynomial O(M2) in order to suit
resource-limited sensors.
4. Unsupervised learning is able to adapt to unforeseeable and new
changes in a non-stable environment.
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18. Reference
• T. Luo and S. G. Nagarajan, "Distributed Anomaly Detection Using
Autoencoder Neural Networks in WSN for IoT," 2018 IEEE
International Conference on Communications (ICC), Kansas City, MO,
2018, pp. 1-6.
doi: 10.1109/ICC.2018.8422402
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