@inproceedings{ad3aa10c0a744b06a7e912619a3bc5b5,
title = "A General Model for Long-short Term Anomaly Generation in Sensory Data",
abstract = "Anomaly detection algorithms play an important role in Internet of Things (IoT) where a significant amount of data is processed every second. The abnormal data can seriously affect the decision-making of data analysts that may lead to system failure. Hence, anomaly detection algorithms are useful tool to identify anomaly. However, detection accuracy of these algorithms is affected by the amount and quality of training data. In fact, the well-known-published datasets are limited. Moreover, they are not labeled and are hard to use for training. In this paper, we propose a general model for artificial anomaly generation. The proposed model can generate six typical forms of anomalies in IoT time-series data including stuck-at, offset, drift, noise, outlier, and spike. The model allows users not only to straightforwardly generate anomalies under various parameters but also generate combined anomalies which are the combination of those six typical forms of anomalies.",
keywords = "anomaly detection, Anomaly generation, fault detection, IoT, security, wireless sensor networks",
author = "Dang, \{Thien Binh\} and Le, \{Duc Tai\} and Moonseong Kim and Hyunseung Choo",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 16th International Conference on Ubiquitous Information Management and Communication, IMCOM 2022 ; Conference date: 03-01-2022 Through 05-01-2022",
year = "2022",
doi = "10.1109/IMCOM53663.2022.9721783",
language = "English",
series = "Proceedings of the 2022 16th International Conference on Ubiquitous Information Management and Communication, IMCOM 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Sukhan Lee and Hyunseung Choo and Roslan Ismail",
booktitle = "Proceedings of the 2022 16th International Conference on Ubiquitous Information Management and Communication, IMCOM 2022",
}