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Irivbogbe, Idehen Jimmy (2021) Securing Internet of things (IoT) using SDN - enabled Deep learning Architecture. Masters thesis, Dublin, National College of Ireland.
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Abstract
In recent years, billions of devices have been connected through the Internet of things (IoT) and continuously shared data. The extreme connectivity of these devices makes the IoT vulnerable to different cyber-attacks, leading to financial and information loss. Due to such threats, the IoT demands a secure infrastructure and is in dire need of security. This work proposes a deep learning model for the detection of cyber threats in IoT. This work used the DNNLSTM algorithm for the detection of threats.
Further, publicly available CICIDS 2018 is used for the training of the proposed algorithm. The proposed model achieved an accuracy of 99.92%, with a recall of 99.50%. This work also compared the proposed model with two other algorithms (GRU and LSTMGRU), trained on the same dataset as well as with existing literature. The proposed model outclassed the other algorithms and existing literature in accuracy and different evaluation metrics.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Clara Chan |
| Date Deposited: | 19 Oct 2021 16:34 |
| Last Modified: | 19 Oct 2021 16:34 |
| URI: | https://norma.ncirl.ie/id/eprint/5113 |
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