Applied Artificial Intelligence

Using Big Data Analytics together with Artificial Intelligence (A.I), this area of research looks into the different means by which the large amounts of data generated by different devices as well as IDS (Intrusion Detection Systems) can be leveraged to detect the presence of cybersecurity attacks in both IoT as well as in traditional computer networks. This involves using different machine and deep learning algorithms to automate the feature learning and detection process.

Current work, led by Dr Thomas Win​, combined the trained model with real-time big data streaming technologies (e.g., Apache Spark) to detect attack presence in the large amounts of data collected. This involves real-time collection of data from various sources within the network, performing statistical analysis of the feature characteristics to generate a feature vector. The generated feature vector is then passed into the trained deep learning model to detect cybersecurity attack presence.

A related PhD study, supervised by Prof Kamal Bechkoum, is about developing a security system on chip for IoT devices. The aim is to develop a solution that enhances security between Edge nodes and Preprocessing Gateways through implementing cryptographic algorithms at the perception Layer.

Prof Bechkoum is also working with Sepideh Mollajafari on “security issues in blockchain technology”.