Journal
COMPUTERS & ELECTRICAL ENGINEERING
Volume 110, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2023.108852
Keywords
Darknet; Deep learning; Network sensing; Adaptive sampling; Reinforcement learning; Monitoring
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The Internet is crucial for global applications and businesses, but security is a major challenge. The Darknet, a parallel network within the Internet, requires real-time classification due to malicious activities. Our paper proposes a novel approach using CNN and RL techniques for intelligent and adaptive packet sampling rates in high-performance networks. With a TOR traffic prediction accuracy of 99.84%, our method shows successful classification in high-throughput networks.
The Internet plays a crucial role in supporting global applications and businesses, but security remains a major challenge. Within the Internet, there exists a parallel network known as the Darknet, where malicious activities and traffic are present and require real-time classification. Many methods aim to classify this Darknet traffic in real-time due to its significant volume within Internet traffic. However, online Darknet traffic classification faces challenges, particularly in determining the optimal packet sampling amount for achieving a high classification rate in high-performance networks. To address this, our paper presents a novel approach that combines Convolutional Neural Network (CNN) and Reinforcement Learning (RL) techniques for intelligent and adaptive packet sampling rates in high-performance network interfaces. This method reduces overhead on monitored entities, especially in high-speed networks with a high bit rate. Our findings demonstrate a TOR traffic prediction accuracy of 99.84% and successful classification tasks in high-throughput networks using our method.
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