Print Email Facebook Twitter Machine Learning-based Techniques for Secure and Efficient IoT Data Management Title Machine Learning-based Techniques for Secure and Efficient IoT Data Management Author Kramer, Tim (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Conti, M. (mentor) Lal, C. (mentor) Martinez, Jorge (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-02-03 Abstract The dramatic increase in the number of Internet of Things (IoT) devices has created rapid growth for exploitation of security flaws and vulnerabilities. Particularly for critical infrastructure and real-time systems security threats can be highly damaging. Machine Learning (ML) algorithms have demonstrated the ability to combat the security threats and improve the efficiency of data management within IoT networks. This paper addresses how ML methods improve security and efficiency. A review of the current approaches is conducted and these approaches are categorized into detection systems as well as privacy and efficiency enhancements. The proposed future research directions are then presented to address the limitations of the state-of-the-art ML-based IoT security methods. Subject Internet of ThingsMachine LearningSecurityEfficiency To reference this document use: http://resolver.tudelft.nl/uuid:776b7c1a-5920-4c1f-887b-a631d49bcbc2 Part of collection Student theses Document type bachelor thesis Rights © 2023 Tim Kramer Files PDF Final_Research_Paper.pdf 182.95 KB Close viewer /islandora/object/uuid:776b7c1a-5920-4c1f-887b-a631d49bcbc2/datastream/OBJ/view