A ligthweight ensemble-based model for malware detection and classification in the IoT environment
Baldoni, Federico Jacopo (A.A. 2023/2024) A ligthweight ensemble-based model for malware detection and classification in the IoT environment. Tesi di Laurea in Data science in action, Luiss Guido Carli, relatore Alessio Martino, pp. 109. [Master's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Malware analysis and malware detection. Machine learning in the field of cybersecurity, malware detection and malware analysis. An introduction to machine learning. How machine learning has become a standard practice in malware detection. Issues and challenges. Trends. Malware analysis economics. Malware in the field of IoT. Related work. Pc-based. IoT. Research question. A lightweight ensemble-based approach to detect and classify malware in IoT devices. A more in-depth analysis of the data. Experimental design.
References
Bibliografia: pp. 68-72.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data science in action |
| Thesis Supervisor: | Martino, Alessio |
| Thesis Co-Supervisor: | Coppa, Emilio |
| Academic Year: | 2023/2024 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 09 Jun 2025 15:39 |
| Last Modified: | 09 Jun 2025 15:39 |
| URI: | https://tesi.luiss.it/id/eprint/42373 |
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