This research will explore a new way of managing some of the cyber security issues related to the use of low power computing and sensor devices, principally those for the Internet of Things (IoT) and Industrial Control Systems (ICS).
Currently large enterprises, service providers and other organisations rely heavily on a legacy model of cyber security threat detection/analysis based on an ’in-band’ management solution which doubles the network bandwidth required to undertake data capture and effectively lowers network efficiency. This places an enormous strain on enterprises worldwide who will need to increase their managed detection and response services from 1% to 15% as the use of Iot/ICS devices reaches an anticipated 26 billion by 2020. Unless new approaches are found to manage IoT/ICS cybersecurity this situation will get worse.
The proposed research will seek to identify how a distributed thin model of real-time Adaptive Data Capture on small footprint devices within an Iot/ICS infrastructure can improve the effectiveness of an organisations threat detection capability so allowing the improved mitigation of risk. The use of intelligent learning systems will feature to allow the adaptive nature of data capture to be become autonomous, so realising benefits such as improved efficiency of a Security Operations Centre, improved response time between infection and detection, and improved pre-forensics capability.
This research has a high potential impact given the field is of paramount importance to the fight against cyber crime by government ’blue lights’ organisations such as police forces and intelligence agencies.
This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website (View Website) as they become available.