Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT

Abstract

Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging the gap between the physical and the cyber worlds. Anomaly detection is a critical task in this context as it is responsible for identifying various events of interests such as equipment faults and undiscovered phenomena. However, this task is challenging because of the elusive nature of anomalies and the volatility of the ambient environments. In a resource-scarce setting like WSN, this challenge is further elevated and weakens the suitability of many existing solutions. In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud respectively, such that (i) anomalies can be detected at sensors in a fully distributed manner without the need for communicating with any other sensors or the cloud, and (ii) the relatively more computationintensive learning task can be handled by the cloud with a much lower (and configurable) frequency. In addition to the minimal communication overhead, the computational load on sensors is also very low (of polynomial complexity) and readily affordable by most COTS sensors. Using a real WSN indoor testbed and sensor data collected over 4 consecutive months, we demonstrate via experiments that our proposed autoencoderbased anomaly detection mechanism achieves high detection accuracy and low false alarm rate. It is also able to adapt to unforeseeable and new changes in a non-stationary environment, thanks to the unsupervised learning feature of our chosen autoencoder neural networks.

Deep Learning in Mobile and Wireless Networking: A Survey

“…Analysis [17], [112], [235]- [266] [73], [97], [187], [267]- [291] Mobility Analysis [227], [292]- [310] User Localization [272], [273], [311]- [315] [111], [316]- [334] Wireless Sensor Networks [335]- [346], [346]- [356] Network Control [186], [293], [357]- [368] [234], [368]- [403] Network Security [185], [345], [404]- [419] [223], [420]- [429], [429]- [436] Signal Processing [378], [380], [437]- [444] [322], [445]- [458] Emerging Applications For each domain, we summarize work broadly in tabular form, providing readers with a general picture of individual topics. Most important works in each domain are discussed in more details in text.…”

Anomaly Detection for IoT Time-Series Data: A Survey

“…A higher reconstruction error suggests that there is some information within the input data which is not expected given the data used to train that network. Autoencoders are placed onto resource constrained sensor devices in [55], each device is responsible for collecting sequential data over a period of time and detecting anomalies based upon the reconstruction error produced by its shallow autoencoder network. Training is performed in a daily batch method in a central cloud location using the reported input and output vectors generated by each sensor.…”

Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

“…A distributed anomaly detection scheme [60] using autoencoder neural networks is proposed for IoT. The scheme detects anomalies by utilizing two algorithms that are placed on the sensors (Distributed Anomaly Detection using Autoencoders-S) and the IoT cloud (Distributed Anomaly Detection using Autoencoders-C).…”