<p dir="ltr">As cyber threats evolve, Power Distribution Systems (PDS) face growing risks from sophisticated attacks like False Data Injection Attacks (FDIAs), which can disrupt system stability and reliability. This thesis presents a quantum-based approach using Quantum Support Vector Machines (QSVM) to detect and mitigate FDIAs in PDS. By leveraging quantum feature mapping, the QSVM model efficiently identifies subtle anomalies within high-dimensional data, enhancing the accuracy and speed of FDIA detection. The methodology includes the integration of an augmented Lagrangian function to further optimize detection performance. Validated using the IEEE-13 bus system, this QSVM framework showcases its potential as a robust, real-time detection tool for cybersecurity in smart grid infrastructures. The results underscore the promise of quantum computing in strengthening the resilience of critical energy systems.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/27976563 |
| Date | 05 December 2024 |
| Creators | Urmisha Reddy Janak (20391372) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/DETECTION_OF_CYBER_ATTACKS_ON_POWER_DISTRIBUTION_SYSTEM_USING_QSVM/27976563 |
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