Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study
This work addresses the challenge of encrypted malicious traffic detection for cybersecurity, but it is incremental as it focuses on review and dataset consolidation rather than novel method development.
The paper tackles the problem of detecting malicious traffic in encrypted data by providing a systematic review of machine learning approaches and creating a comprehensive dataset from five sources to enable fair comparison of 10 detection algorithms.
As people's demand for personal privacy and data security becomes a priority, encrypted traffic has become mainstream in the cyber world. However, traffic encryption is also shielding malicious and illegal traffic introduced by adversaries, from being detected. This is especially so in the post-COVID-19 environment where malicious traffic encryption is growing rapidly. Common security solutions that rely on plain payload content analysis such as deep packet inspection are rendered useless. Thus, machine learning based approaches have become an important direction for encrypted malicious traffic detection. In this paper, we formulate a universal framework of machine learning based encrypted malicious traffic detection techniques and provided a systematic review. Furthermore, current research adopts different datasets to train their models due to the lack of well-recognized datasets and feature sets. As a result, their model performance cannot be compared and analyzed reliably. Therefore, in this paper, we analyse, process and combine datasets from 5 different sources to generate a comprehensive and fair dataset to aid future research in this field. On this basis, we also implement and compare 10 encrypted malicious traffic detection algorithms. We then discuss challenges and propose future directions of research.