Applied Comparative Evaluation of the Metasploit Evasion Module

Casey, Peter; Topor, Mateusz; Hennessy, Emily A.; Alrabaee, Saed; Aloqaily, Moayad; Boukerche, Azzedine

doi:10.1109/iscc47284.2019.8969663

Cited by 9 publications

(1 citation statement)

References 7 publications

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“…Flow-based machine-learning techniques have emerged as the predominant way of classifying encrypted information. Nevertheless, it is important to highlight that the gathering of training datasets and the use of features for detecting encrypted communication are still subjects of intense investigation [ 22 , 23 , 24 , 25 , 26 ].…”

Section: Related Workmentioning

confidence: 99%

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning

Alwhbi,

Zou,

Alharbi

2024

Sensors

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Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis.

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