Machine learning based cyber security in IoT appliances


   Faculty of Engineering, Computing and the Environment

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  Dr J Kiruthika, Dr M Davis, Prof JC Nebel  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Internet of Things (IoTs) appliances are integrated in our day-to-day life needing a secure platform for us to be comfortable in using them. IoT devices seamlessly fit into our daily life with minimum or no change to it thereby saving time and money. As the IoT appliances are used by common masses due to its commercial availability, it is applied on many areas including hospital care, social care, education etc. driving the industries to focus on making them secure so as to build trust and maintain user’s privacy. Furthermore IoT devices produce big data that can be analysed.

By applying machine learning algorithms on real-time data from such devices a user’s daily life can be inferred using training data after it has been accumulated for an initial period of time. This data can be used to analyse the daily routine of a user’s behaviour and this project focuses on analysing such user behaviour and to map out the triggers which causes them.

In addition, this project explores the security of the appliances used by the IoT appliances and the user behaviour results, anomalies to normal behaviour can be mapped out resulting in creating a smart alert system. This can be applied to remotely monitor vulnerable/elderly users or in assisted living care resulting in reduction of public resources. It also proposes a secure solution using multiple approaches a user can validate his/her credentials for secure access.


Funding Notes

No funding is available for this project
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