Trust and Reputation Management for Blockchain-enabled IoT

In recent years, there has been an increasing interest in incorporating blockchain for the Internet of Things (IoT) to address the inherent issues of IoT, such as single point of failure and data silos. However, blockchain alone cannot ascertain the authenticity and veracity of the data coming from IoT devices. The append-only nature of blockchain exacerbates this issue, as it would not be possible to alter the data once recorded on-chain. Trust and Reputation Management (TRM) is an effective approach to overcome the aforementioned trust issues. However, designing TRM frameworks for blockchain-enabled IoT applications is a non-trivial task, as each application has its unique trust challenges with their unique features and requirements. In this paper, we present our experiences in designing TRM framework for various blockchain-enabled IoT applications to provide insights and highlight open research challenges for future opportunities.Comment: COMSNETS 2023 Invited Pape

Detection of False Data Injection Attacks in Multi-Microgrid

In this thesis an Intrusion Detection System was developed to fight False Data Injection Attacks in Multi-Microgrids. Multi-Microgrids are a part of future power systems and they form the core part of critical infrastructure where resiliency and availability are exceedingly important. Severe consequences in the main power grid can happen if security is not taken into account. The Energy Management System has to be protected against cyber-attacks and one of the dire threats is a False Data Injection Attack. False Data Injections in Energy Management Systems are among the critical threats that need to be taken seriously as they can cause a major harm. In this thesis, the impact of a False Data Injection Attack on Multi-Microgrids and Energy Management Systems has been explored. It has also been researched how to detect these attacks by designing and developing a Multi-Microgrid model in MATLAB/Simulink for emulating the operation of Multi-Microgrid. The MATLAB/Simulink model simulates a Multi-Microgrid environment over the course of 24 hours. To detect False Data Injection Attacks from the data created in this simulation a Kalman Filter based Intrusion Detection System was developed. The Kalman Filter based Intrusion Detection System analyzes simulation data for possible False Data Injection Attacks. Further analysis was done based on the results of the Kalman Filter based Intrusion Detection System implementation. The implementation was tested with a set of attack simulations. The results analysis revealed that developed Kalman Filter based Intrusion Detection System is suitable for detecting simple attacks but it has low accuracy for complex intrusion attacks. With taking into account only the types of attacks the implementation was initially planned to detect the detection rate averaged to 87 %. The detection accuracy could be improved in future work by considering complex attack types early on in the implementation of the detection system. Securing power systems against malicious actors from causing harm or gaining financial benefits is a far-reaching research topic with plenty of future paths to explore. Kalman Filter based methods are one of the potential methods for detecting False Data Injection Attacks in Energy Management Systems. More research on Kalman Filter based protections is part of the ongoing race in protecting ourselves from cyber-attacks against critical infrastructure

Dynamic Trust Management

Trust management forms the basis for communicating policy among system elements and demands credential checking for access to all virtual private service resources—along with careful evaluation of credentials against specified policies—before a party can be trusted