Name
Title | ||
|---|---|---|
Leveraging Semisupervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT Communication |
Abstract | ||
|---|---|---|
The rapid development of the Internet of Things (IoT) accumulates a large number of communication records, which are utilized for anomaly detection in IoT communication. However, only a small part of these records can be labeled, which increases the difficulty in anomaly detection. This article proposes a semisupervised hierarchical stacking temporal convolutional network (HS-TCN), which is the first semisupervised model for anomaly detection in IoT communication, and it can train unlabeled data based on a small number of labeled data. Furthermore, HS-TCN fully considers the features of streaming data in IoT communication and can weed out uncertain records. Finally, the experimental results demonstrate that HS-TCN promotes the performance of anomaly detection and attains better efficiency. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JIOT.2020.3000771 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Anomaly detection,Internet of Things (IoT),semisupervised model,temporal convolutional network (TCN) | Journal | 8 |
Issue | ISSN | Citations |
1 | 2327-4662 | 2 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongliang Cheng | 1 | 2 | 0.70 |
| Yan Xu | 2 | 63 | 9.97 |
| Hong Zhong | 3 | 208 | 33.15 |
| Yi Liu | 4 | 45 | 5.66 |