Blockchain technology offers a safe and innovative way to realise the concept of self-sovereignty, which refers to an individual's complete control over and ownership of their personhood. Our intrinsic freedom to assert ourselves and choose who we are in the world gives rise to our self-sovereign identity. We have relied on governments to protect that right and enforce that power until now. We will soon be able to create and manage our own digital identities using distributed ledgers, and we will be able to do it with ease. Blockchains are a novel digital technology that combines peer-to-peer network computing and cryptography to produce a decentralised, immutable public ledger. A blockchain is a cryptocurrency, such as Bitcoin, where the ledger records money; however, ledger entries can record any data structure, such as property titles, identity and certification, contracts, and many more. Decentralized financial services, enabled by blockchain technology, can increase financial inclusion, facilitate open access, stimulate permissionless innovation, and provide new opportunities for entrepreneurs and innovators. The rapid evolution of the ecosystem has made it difficult to grasp the fundamental principles of these systems and the threats they pose to security. In this work, I will provide you with a thorough overview of blockchain, as well as examine smart contract security challenges and how quantum computing may impact blockchain security.

Detecting anomalies in a smart grid system has become critical due to the increase in cyberattacks. The fuzzy and dynamic nature of cyberattacks has caused a lot of challenges in detecting anomalies leading to abnormal predictions in a smart grid system. That has allowed cyber criminals to deploy various attacks on the network. Due to the rapidly expanding industry of cyber-physical systems, the traditional power grid has been upgraded into a communication infrastructure network. However, this upgrade has led to cyberattacks such as Ukraine power system cyberattack 2015, Iran Aramco attack 2017 and New York smart grid system attack 2018 causing a lot of damage to the systems, the economy and livelihoods. The aim of this dissertation is to explore anomaly detections in smart grid systems using machine learning models. Due to the integrated nature of the smart grid system, it has inherent vulnerabilities which attackers can exploit because of its integrated design. Such threat actors take advantage of these flaws, which has an enormous effect on embedded systems. This problem is rampant because of the potentially extensive size of these networks, which makes continuous monitoring by technicians or specialists impracticable. For this problem to be effectively resolved, automatic detection of entities that present anomalous activity is essential. Machine learning methods are important in transforming the security of smart grid systems from merely facilitating secure communication between devices to security-based intelligence systems. The dissertation proposes integrating multiple supervised machine learning-based models into a single Intrusion Detection Systems to complement each other and mitigate the shortcomings of the other models that will be suitable to detect anomalies in the smart grid communication network effectively.

The smart grid is an extremely complex system of immense importance in people's modern lives. The modernisation of the smart grid has a direct impact on economic, political, and social aspects. While the modernisation of the electricity infrastructure has many benefits, it also brings many new threats. In addition, one must consider the increasing number of attacks on the smart grid, such as cyber security attacks, physical blackouts, and false data injection attacks. This article will describe how sensor monitoring can be used to improve the security of the smart grid, as well as Internet-based change attacks against the smart grid. Finally, future developments and potential problems for the smart grid are presented and discussed.