University of Technology Sydney. Faculty of Engineering and Information Technology.Nowadays, the Internet of Things (IoT) connects billions of devices (things) using the Internet. The devices could be sensors, actuators etc. The number of IoT devices growing and interacting with each other raises the issues of security and trust. Most of the existing trust and security solutions do not present a comprehensive trust management solution for IoT addressing key trust management issues for the IoT. Many of the current solutions do not consider the scalability of the IoT trust management solution. With the rapid growth of IoT nodes a significant majority of the existing techniques do not address methods (or algorithms) to detect uncompliant behaviour or attacks on the trust management approach by the IoT nodes. The uncompliant behaviour may take the form or bad-mouthing attacks, on-off attacks, contradictory attacks and bad service attacks. In the existing literature there is no trust management approach that is scalable and resilient against attacks by uncompliant IoT nodes.
To address the above mentioned gaps in the existing literature body, in this thesis I propose an intelligent trust management platform for IoT (TM-IoT). The TM-IoT solution is centred on trust-based clustering of the IoT nodes. IoT nodes are grouped into clusters and each cluster is managed by a Master Node (MN). MN is a responsible for all the trust management activities within each cluster. The Super Node (SN) oversees and manages the MNs. Intelligent fuzzy-logic based and non-fuzzy logic based algorithms are presented to counter untrustworthy IoT nodes from carrying out attacks such as bad-mouthing attacks, on-off attacks, contradictory attacks and bad service attacks.
To validate the proposed solutions in this thesis, simulations were conducted using Contiki network simulator for IoT environment (Cooja), which able to simulate large networks. Using the built prototype, I have evaluated and simulated our proposed solutions for the above-mentioned problems by using Cooja and C++ programming language. The obtained results demonstrate the effectiveness of the TM-IoT and also that of the algorithms in achieving their respective goals
