WIReDSpace
metadata only
Revolutionising Electricity Theft Detection in Smart Grids: An Edge-Centric Hybrid Machine Learning Framework with IoT Integration
A Dissertation submitted in fulfillment of the requirements for the degree of Master of Science in Engineering, to the Faculty of Engineering and the Built Environment, School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Johannesburg, 2025
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Hardware-assisted Machine Learning in Resource-constrained IoT Environments for Security: Review and Future Prospective#133994
- Detection of Security Attacks in Industrial IoT Networks: A Blockchain and Machine Learning Approach#134038
- Machine learning based approach to intrusion detection in internet of things environments.#150216
- EnThM: Energy Theft Mitigation in Smart Grids using Hierarchical Verification of Metering Data#224419
- Detection of disturbances and cyber-attacks in smart grids using explainable machine learning.#1183
Record · ID 1242153
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive