<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Although the Internet of Things (IoT) is a rapidly developing technology, it also brings a number of security challenges, such as IoT attacks. Currently, research on IoT anomaly detection in Software-Defined Networking (SDN) relies only on the control plane. In this study, we aim to detect IoT anomalies by covering the advantages of the control and data plane. First, we collected real-time network telemetry data from the data plane based on the capabilities of the P4. Then, using this telemetry data, we built different anomaly detection models and compared their performance. Among them, the one-Dimensional Convolutional Neural Network (1D CNN) model classified our data best and showed the highest performance, so we proposed this model for IoT anomaly detection on the control plane. To our knowledge, our approach is the first solution that integrates the control plane and data plane for IoT anomaly detection. Finally, when evaluating the performance of our proposed 1D CNN model, the accuracy, F1 score, and Matthews correlation coefficient (MCC) are the same or better than existing studies.</p>
</abstract>

Although the Internet of Things (IoT) is a rapidly developing technology, it also brings a number of security challenges, such as IoT attacks. Currently, research on IoT anomaly detection in Software-Defined Networking (SDN) relies only on the control plane. In this study, we aim to detect IoT anomalies by covering the advantages of the control and data plane. First, we collected real-time network telemetry data from the data plane based on the capabilities of the P4. Then, using this telemetry data, we built different anomaly detection models and compared their performance. Among them, the one-Dimensional Convolutional Neural Network (1D CNN) model classified our data best and showed the highest performance, so we proposed this model for IoT anomaly detection on the control plane. To our knowledge, our approach is the first solution that integrates the control plane and data plane for IoT anomaly detection. Finally, when evaluating the performance of our proposed 1D CNN model, the accuracy, F1 score, and Matthews correlation coefficient (MCC) are the same or better than existing studies.

IoT Anomaly Detection with 1D CNN Using P4 Capabilities

Department of Programming languages and Compilers, Faculty of Informatics

Department of Information Network and Cyber Security

Mongolian University of Science and Technology (MUST)

Acta Electrotechnica et Informatica

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"IoT Anomaly Detection with 1D CNN Using P4 Capabilities"}

Although the Internet of Things (IoT) is a rapidly developing technology, it also brings a number of security challenges, such as IoT attacks. Currently,...

IoT Anomaly Detection with 1D CNN Using P4 Capabilities

Online veröffentlicht: 08. Aug. 2023

Seitenbereich: 3 - 12

Eingereicht: 12. Apr. 2023

Akzeptiert: 16. Juni 2023

DOI: https://doi.org/10.2478/aei-2023-0006

Schlüsselwörter
P4, In-Band Network Telemetry(INT), IoT security, 1D-CNN, SDN

© 2023 Gereltsetseg Altangerel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

IoT Anomaly Detection with 1D CNN Using P4 Capabilities

Online veröffentlicht: 08. Aug. 2023

Seitenbereich: 3 - 12

Eingereicht: 12. Apr. 2023

Akzeptiert: 16. Juni 2023

DOI: https://doi.org/10.2478/aei-2023-0006

SchlüsselwörterP4, In-Band Network Telemetry(INT), IoT security, 1D-CNN, SDN

© 2023 Gereltsetseg Altangerel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
P4, In-Band Network Telemetry(INT), IoT security, 1D-CNN, SDN