A Study on Anomaly Detection with Deep Learning Models for IoT Time Series Sensor Data

• Published in: International Conference on Signal Processing and Communication (Online) pp. 11 - 14
• Main Authors: Sachdeva, Ujjwal, Vamsi, P. Raghu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2022
• Subjects: Anomaly; Artificial neural networks; Deep learning; GAN; Internet of Things; Machine learning algorithms; OCSVM; sensor data; Statistical learning; Support vector machines; time series; Time series analysis; Training; Variational Autoencoder
• ISSN: 2643-444X
• DOI: 10.1109/ICSC56524.2022.10009580

The high likelihood of noise and the lack of labels in Internet of Things (IoT) time-series sensor readings make the detection of abnormalities an important subject of study. When there is a high correlation between sensor data points, conventional data mining and machine learning algorithms are unable to detect abnormalities. Furthermore, the amount and speed with which IoT sensors report data is one of the reasons why traditional machine and statistical learning algorithms are unable to detect anomalies. In recent years, Artificial Neural Networks (ANN) have been widely used for developing Deep Learning models for learning and classifying unlabeled data for detecting anomalies with high accuracy. In this connection, this paper presents a study on the performance of two well-known ANN models, such as Generative Adversarial Network (GAN) and Variational Auto Encoder (VAE), and a classification model, One Class Support Vector Machine (OCSVM). To evaluate performance, simulations were conducted using a well-known network and sensor datasets. The outcomes show which technique outperformed the others in terms of detection accuracy and training duration.