Keston, Geoff2023-04-112023-04-112023http://hdl.handle.net/20.500.12613/8409This research project was completed as part of the Technical Communication (ENGR 2196) course.Although the increasing integration of Internet of Things devices into the modern grid infrastructure has improved grid performance and efficiency in many ways, cyberattacks now pose a significant threat to system stability and reliability. For instance, false data injection attacks modify sensor measurements and control signals, disrupting power balancing and supplication tasks performed by generation control systems. Detecting these attacks can mitigate their impact. In this paper, three machine learning detection methods are comparatively analyzed to determine implementation efficacy and practicality: long short-term memory, generative adversarial networks, and cluster-driven ensemble learning. The novel cluster-driven ensemble learning algorithm (and its associated intrusion detection system) best satisfies the evaluation criteria due to its accuracy, resource requirements, and decentralized architecture. Additionally, this paper proposes a framework for an open-source, portable cyber-physical testbed using the SEED Internet Emulator. With the framework described, the SEED Emulator can be used to address the lack of existing cybersecurity testbed platforms for grid applications and can be applied to verify the efficacy of the proposed solution. A successful implementation of the cluster-driven ensemble learning intrusion detection system will improve grid security and stability, mitigating the costly social, economic, and environmental consequences of data injection attacks.49 pagesengIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.http://rightsstatements.org/vocab/InC/1.0/Smart gridMachine learning (ML)Deep learning (DL)False data injection (FDI)Automatic generation control (AGC)CybersecurityLong short-term memory (LSTM)Generative adversarial network (GAN)Cluster-driven ensemble learning (CDEL)Internet of Things (IoT)Text