AbstractInternet of Things (IoT) sensor devices generate data based on observations from
the surroundings and feed it back to the cloud for processing and decision making.
Considering the importance of IoT data, it is crucial to ensure the security of data generated by IoT devices. However, the task of deploying security becomes challenging due to the complex nature of the IoT framework; (i) IoT sensors are
heterogeneous, (ii) IoT sensors have limited resources, (iii) the size of the IoT network is not fixed, and (iv) the data produced by IoT devices is in bulk. This creates an opportunity for researchers to coin novel cryptographic solutions that can provide sufficient security for IoT platform, despite the discussed challenges.
The cryptographic algorithms used in practice like AES or RSA are standardised algorithms whose architecture and working mechanisms are public. The only thing that keeps data secure is the secret cryptographic key. However, the strength of cryptographic keys relies on the seed value used to generate the secret key. It is harder to predict a cryptographic key that is generated using a random seed value, however, keys generated using a defined pattern are susceptible to various types of attacks that are based on guessing the pattern of the seed.
In this thesis, novel pseudo random number generators (PRNGs) have been proposed that allow IoT devices to generate fresh keystreams based on random seed values. The random number generators have been thoroughly studied using statistical analysis, entropy analysis, randomness testing and security analysis. The performance of proposed PRNGs have been examined using studied on IoT sensors with varying range of resource capabilities. The proposed PRNGs have been used in real encryption applications for images and has been tested against all major attack models.
The final part of this thesis summarises some interesting applications of cryptography that have been proposed to solve industry based security problems in IoT frameworks.
|Date of Award||Oct 2022|
|Supervisor||Bryan Scotney (Supervisor), Mark Mc Cartney (Supervisor) & Jorge Martinez Carracedo (Supervisor)|
- Random number generators