Faculty of Science and Engineering
School of Computing, Electronics and Mathematics
Funded PhD Research Studentship
Advanced cyber-attack detection and mitigation system for IoT devices
The grand vision of the Internet of Things (IoT) is to establish a whole new ecosystem comprised of heterogeneous connected devices – computers, laptops, smartphones and tablets, as well as, embedded devices and sensors – that communicate to deliver environments making our living, cities, transport, energy and many other areas more intelligent. Earlier forecasts on the number of connected devices estimated their number around 50 billion by 2020.
Such technological evolution is also making our society vulnerable to
new forms of threats and attacks exploiting the complexity and heterogeneity of
IoT networks, therefore rendering cyber-security amongst the most important
aspects of a networked world. As the networked devices become ubiquitous,
cyber-attacks will become more frequent and even more sophisticated. There are
already numerous recent examples of cyber-attacks that exploit the internet-connected appliances, such as refrigerators, televisions, cameras and
cars, for example in order to perform denial-of-service (DoS) or distributed DoS
(DDoS) attacks of unprecedented scales, spy on people in their office/homes,
and take over (hijack) communication links thus delivering full control of
anything that is remotely controlled, like drones and vehicles, to
Computer-controlled devices in automobiles, like locks, brakes and engines, have been shown to be vulnerable to numerous attacks. These devices are currently not connected to external computer networks, and hence are less vulnerable to internet attacks. If connected to the internet, then attacks where the steering wheel is turned while driving, or the doors get unlocked would become a reality. The threat is relevant to the health sector as well, where potentially deadly vulnerabilities have been found in a large number of medical devices, including insulin pumps, CT-scanners, implantable defibrillators and x-ray systems.
The proposed research aims to explore IoT vulnerabilities and propose and innovative gateway that will utilise Deep Learning, fuzzy and deep packet inspection in order to detecting and mitigate Botnets that use IoT devices for DDoS and RoQ attacks.