Honeypot is a decoy system with vulnerabilities introduced to trap hackers. Through many years of evolution, a generation of smart honeypots has been developed. The self-adaptive honeypot is a smart honeypot that is expected to respond appropriately to the attacker's request. In most existing self-adaptive honeypot systems, the commands sent from the attacker play a central role in the reasoning process of the honeypot. In this thesis, we focus on the construction of a machine learning workflow that aims at estimating the risk level of these commands. Experiments show that the proposed workflow achieves potential results.
| Date of Award | 4 Sept 2020 |
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| Original language | English |
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| Awarding Institution | |
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| Supervisor | Jean-Noel Colin (Supervisor) |
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- honeypot
- machine learning
- representation learning
- labeling model
- classification
Classification of Linux Commands in SSH Session by Risk Levels
Thuy Ngan, D. (Author). 4 Sept 2020
Student thesis: Master types › Master in Computer Science Professional focus in Data Science