Increasing communication security among internet of things

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Dokuz Eylül Üniversitesi, Fen Bilimleri Enstitüsü, Türkiye

Tezin Onay Tarihi: 2022

Tezin Dili: İngilizce

Öğrenci: MURAT EMEÇ

Danışman: Mehmet Hilal Özcanhan

Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu

Özet:

The development of information technologies makes significant contributions to many aspects of our life. It brings many innovations together, depending on the rate of technology's growth. The Internet of Things (IoT) is among the most popular and fastest expanding technologies, in recent years. Addressable IoT devices generate and use significant data over the Internet. In addition to increasing data traffic, attacks in IoT networks are also increasing, significantly.

 

The present thesis increases security in IoT communication, by providing binary and multi-label classification methods for identifying attacks in IoT networks. A new Hybrid Deep Learning model has been designed for detecting intrusions. Two different public datasets (CIC-IDS-2018, BoT-IoT) are used for the proposed Intrusion Detection System (IDS). In addition, a new dataset containing routing attacks targeting IoT devices has been created, because of the increase in routing attacks in IoT networks, in recent years. Hence, a total of 14 attack types have been analyzed in the present work. The results of the analysis have been presented extensively, as to the accuracy, F1-score, and training time of the model.

 

Comparing our results to previous studies showed that our designed hybrid Deep Learning model has the best training time/accuracy and time/F1-score performance ratios. Comparisons prove that our proposed model is more successful in detecting IoT attacks than the previous works. The successful performance of our proposed model is proof that hybrid Deep Learning methods can be an innovative and efficient perspective in IoT Intrusion Detection Systems.