| Title | Scalable Wi-Fi Intrusion Detection for IoT Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Örs, Faik Kerem, Aydın, Mustafa, Boğatarkan, Aysu, Levi, Albert |
| Conference Name | 2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS) |
| Keywords | anomaly detection, attack classification, Data models, Internet of Things, Internet of Things (IoT), Intrusion detection, machine learning, Protocols, pubcrawl, Scalability, Scalable Security, Wi-Fi Security, Wireless communication, Wireless Network Security, Zigbee |
| Abstract | The pervasive and resource-constrained nature of Internet of Things (IoT) devices makes them attractive to be targeted by different means of cyber threats. There are a vast amount of botnets being deployed every day that aim to increase their presence on the Internet for realizing malicious activities with the help of the compromised interconnected devices. Therefore, monitoring IoT networks using intrusion detection systems is one of the major countermeasures against such threats. In this work, we present a machine learning based Wi-Fi intrusion detection system developed specifically for IoT devices. We show that a single multi-class classifier, which operates on the encrypted data collected from the wireless data link layer, is able to detect the benign traffic and six types of IoT attacks with an overall accuracy of 96.85%. Our model is a scalable one since there is no need to train different classifiers for different IoT devices. We also present an alternative attack classifier that outperforms the attack classification model which has been developed in an existing study using the same dataset. |
| DOI | 10.1109/NTMS49979.2021.9432662 |
| Citation Key | ors_scalable_2021 |
Scalable Wi-Fi Intrusion Detection for IoT Systems