An Intelligent, Secured Lightweight Algorithm for 6G IoT apps

6G incorporates software-defined networking (SDN) and network function virtualisation (NFV) technologies to enable dynamic security orchestration. This approach allows security policies to be dynamically adjusted based on the changing threat landscape, application requirements, and network conditions. Intelligent algorithms analyse network traffic and security events to update security policies in real-time automatically. Software-defined integrated intelligent lightweight security techniques provide robust protection for IoT applications while considering IoT devices and networks' unique characteristics and constraints. These techniques leverage AI, Machine Learning (ML), encryption, secure authentication, dynamic orchestration, and context awareness to safeguard IoT devices, data, and infrastructure from emerging security threats. Designing a hybrid SDN-based and lightweight security model for real-time covering IoT apps is not an easy task. It needs to deal with IoT interoperability, heterogeneity, and resource constraints. IoT-developing companies designed tools to monitor and manage such devices' limited computational power, memory, and energy resources monitor and manage limited computational power, memory, and energy resources of such devices. Such models applied recent lightweight security techniques, providing adequate protection and minimizing resource consumption. However, most methods lack jointly covering interoperability and heterogeneity of the IoT devices. Newly developed cryptography can be classified into two main types: symmetric and asymmetric. Symmetric algorithms, such as block ciphers and stream ciphers, fall under the category of symmetric encryption, while asymmetric encryption is represented by ECC (Elliptic Curve Cryptography). Symmetric ciphers employ shorter key lengths in comparison to asymmetric algorithms, which makes them more susceptible to security attacks due to their relatively simpler nature. On the other hand, asymmetric ciphers employ greater complexity to ensure secure IoT network communications, albeit at the cost of slower processing speeds due to larger key lengths. It is essential to thoroughly investigate these critical factors to design an algorithm that offers reduced power consumption, decreased complexity, faster processing times, and adequate security for low-end IoT devices. This thesis will investigate this using enhanced AI/ML methods to mitigate the above issues in the large-scale environment using a 6G ecosystem.

Requirements: UK honours equivalent in Computer Science, Maths, Engineering

Subject areas: Adversarial Machine Learning, SDN, IoT, and 6G

For further details contact: Dr Zahra Pooranian @ z.pooranian@reading.ac.uk