Two-Level Machine Learning for Network Enabled Devices Identification
| Autor: | Wu, Ming-Lun, 吳明倫 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 With the rapid development of Internet of Things technology, the number of network enabled devices on the Internet has exploded and the services provided have become more diverse, making people's lives more convenient. However, the poor design of network enabled devices and the lack of security protection capabilities have led to an endless stream of equipment exploits by hackers, which has led to major security threats to home and corporate network environments that are full of network enabled devices. In order to understand how many potentially network enabled devices are connected to the target network, it is the first step of security protection to understand the network status through network enabled devices identification. This study hopes to explore the technology of two-level machine learning, which is used to process network enabled devices with large volume and hierarchical structure data characteristics, then compare differences with common single-level machine learning. Combined with the concept of semi-supervised learning to explore the possibility of automatically classifying objects which are classified as unknown device. This study uses the Censys network scan dataset to perform binary classifier training with Support Vector Machine and Random Forest classification algorithms, and then classifies the network enabled devices. With semi-supervised learning concepts, trying to find out the best parameters for classified unknown devices by density-based clustering algorithms. Finally, through a number of simulation experiments to verify and compare the differences between the two classification algorithms and single-level and two-level machine learning in this application problem, then provides relevant quantitative and qualitative observations on the experimental results. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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