The Internet of Things (IoT) has become a pervasive technology with various applications ranging from smart homes and cities to industrial automation and healthcare. However, the increasing adoption of IoT devices has also raised significant concerns about cybersecurity and privacy. Blockchain, as a distributed and immutable ledger technology, has been proposed as a potential solution to enhance the security and privacy of IoT systems. Blockchain-based IoT systems offer several benefits, such as decentralization, transparency, and data integrity. However, they also pose unique cybersecurity challenges that need to be addressed for their secure and reliable deployment. In this paper, we review the existing literature and highlight the key challenges in cybersecurity for blockchain-based IoT systems. We categorize these challenges into three main areas: (i) IoT device security, (ii) blockchain security, and (iii) integration of IoT devices with blockchain (network security). Through an in-depth analysis, we present the current state of research and discuss potential solutions for each challenge. Additionally, we contribute by identifying future research directions to address these challenges and enhance the cybersecurity of blockchain-based IoT systems.

Virtual Power Plants (VPPs) have emerged as dynamic solutions, harnessing the distributed resources such as solar panel and batteries. However, the complicated interconnectedness of VPPs exposes them to unique cybersecurity vulnerabilities, distinguishing them from traditional smart grids. This systematic literature review primarily focuses on the cybersecurity challenges VPPs face, such as data breaches and data flow manipulation. The study explores these unique risks based on proposed solutions from the existing literature like federated learning, intrusion detection systems, blockchain integration, and machine learning by analyzing their strengths, weaknesses, and applicability to VPP cybersecurity. Additionally, regulatory barriers and opportunities like cost-savings are discussed, with a focus on ensuring the secure future of VPPs through machine and deep learning and strong security policies. This paper contributes significantly to the field by identifying VPP cybersecurity risks, evaluating potential solutions, and proposing future research directions.