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Investigating the Information Leakage of Transport Layer Security Protocol using Deep Learning and Machine Learning Interpretation Methods

Sadeghian, Zeinab | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57122 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Jafari Siavoshani, Mahdi
  7. Abstract:
  8. Machine learning models and deep learning, in attempting to solve complex and nonlinear problems, are not easily understandable, even for experts in these fields, due to the complexity of functions and issues involved. This lack of interpretability includes how models make decisions and their logical reasoning. Therefore, interpretability methods have gained attention in recent years. On the other hand, machine learning has entered many domains and penetrated a wide range of problems in various fields, especially in computer networks. This is crucial for internet service providers and computer network managers. Solving these problems enables the analysis of data flow structures in the network and its relationship with various applications. Particularly, it allows obtaining information about the protocols present in the network, in addition to understanding the systems themselves. Many of these methods are implemented on encrypted data. The present research focuses on examining information leakage in the Transport Layer Security (TLS) protocol. The results indicate that the initial vector part of encrypted packets in this protocol leaks information, and removing this part leads to a decrease in the accuracy of the learning models. As a result, the accuracy of these models becomes close to that of random models
  9. Keywords:
  10. Computer Networks ; Network Protocol ; Classify Network Traffic ; Deep Learning ; Machine Learning ; Interpretation ; Transport Layer Security (TLS)

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