A Novel SDN Dataset for Intrusion Detection in IoT Networks

Sarica, Alper Kaan
Angın, Pelin
The number of Internet of Things (IoT) devices and the use cases they aim to support have increased sharply in the past decade with the rapid developments in wireless networking infrastructures. Despite many advantages, the widespread use of IoT has also created a large attack surface frequently exploited by cyber criminals, requiring real-time, automated detection and mitigation of various attacks in the high-volume network traffic generated. Software-defined networking (SDN) and machine learning (ML) based intrusion detection are effective tools for providing quick response to various attacks in IoT networks, however the study of ML-based intrusion detection so far has been limited to performance studies on datasets that were created a long while ago and are not specific to SDN-based environments. In this paper we introduce a novel dataset for intrusion detection in IoT networks. The dataset comprises two parts modeling static and dynamic IoT networks and consists of 27.9 million and 30.2 million data records respectively, which contain cyber attacks of various types in addition to benign traffic. The dataset will be an important resource for intrusion detection research in SDN-managed IoT, which will be increasingly prevalent in the future networks of ubiquitous connectivity.
Citation Formats
A. K. Sarica and P. Angın, “A Novel SDN Dataset for Intrusion Detection in IoT Networks,” presented at the 16th International Conference on Network and Service Management (CNSM), (NOV 02-06, 2020), ELECTR NETWORK, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/74753.