We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning.
- Authors
Alwhbi, Ibrahim A.; Zou, Cliff C.; Alharbi, Reem N.
- Abstract
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.
- Subjects
COMPUTER network traffic; MACHINE learning; CLASSIFICATION; INTERNET traffic; MOBILE apps; DATA transmission systems
- Publication
Sensors (14248220), 2024, Vol 24, Issue 11, p3509
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s24113509