Feature Mining for Encrypted Malicious Traffic Detection with Deep Learning and Other Machine Learning Algorithms

• URL: http://arxiv.org/abs/2304.03691v1
• Date: Fri, 7 Apr 2023 15:25:36 GMT
• Title: Feature Mining for Encrypted Malicious Traffic Detection with Deep Learning and Other Machine Learning Algorithms
• Authors: Zihao Wang, Vrizlynn L. L. Thing
• Abstract summary: The popularity of encryption mechanisms poses a great challenge to malicious traffic detection. Traditional detection techniques cannot work without the decryption of encrypted traffic. In this paper, we provide an in-depth analysis of traffic features and compare different state-of-the-art traffic feature creation approaches. We propose a novel concept for encrypted traffic feature which is specifically designed for encrypted malicious traffic analysis.
• Score: 7.404682407709988
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The popularity of encryption mechanisms poses a great challenge to malicious traffic detection. The reason is traditional detection techniques cannot work without the decryption of encrypted traffic. Currently, research on encrypted malicious traffic detection without decryption has focused on feature extraction and the choice of machine learning or deep learning algorithms. In this paper, we first provide an in-depth analysis of traffic features and compare different state-of-the-art traffic feature creation approaches, while proposing a novel concept for encrypted traffic feature which is specifically designed for encrypted malicious traffic analysis. In addition, we propose a framework for encrypted malicious traffic detection. The framework is a two-layer detection framework which consists of both deep learning and traditional machine learning algorithms. Through comparative experiments, it outperforms classical deep learning and traditional machine learning algorithms, such as ResNet and Random Forest. Moreover, to provide sufficient training data for the deep learning model, we also curate a dataset composed entirely of public datasets. The composed dataset is more comprehensive than using any public dataset alone. Lastly, we discuss the future directions of this research.

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