Feature Analysis of Encrypted Malicious Traffic

• URL: http://arxiv.org/abs/2312.04596v1
• Date: Wed, 6 Dec 2023 12:04:28 GMT
• Title: Feature Analysis of Encrypted Malicious Traffic
• Authors: Anish Singh Shekhawat and Fabio Di Troia and Mark Stamp
• Abstract summary: In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication. Antivirus software and firewalls typically will not have access to encryption keys, and therefore direct detection of encrypted data is unlikely to succeed. Previous work has shown that traffic analysis can provide indications of malicious intent, even in cases where the underlying data remains encrypted.
• Score: 3.3148826359547514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication. Antivirus software and firewalls typically will not have access to encryption keys, and therefore direct detection of malicious encrypted data is unlikely to succeed. However, previous work has shown that traffic analysis can provide indications of malicious intent, even in cases where the underlying data remains encrypted. In this paper, we apply three machine learning techniques to the problem of distinguishing malicious encrypted HTTP traffic from benign encrypted traffic and obtain results comparable to previous work. We then consider the problem of feature analysis in some detail. Previous work has often relied on human expertise to determine the most useful and informative features in this problem domain. We demonstrate that such feature-related information can be obtained directly from machine learning models themselves. We argue that such a machine learning based approach to feature analysis is preferable, as it is more reliable, and we can, for example, uncover relatively unintuitive interactions between features.

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