Security Service-aware Reinforcement Learning for Efficient Network Service Provisioning

Jo, Hyeon-Jun; Kim, Kyungbaek

doi:10.23919/apnoms56106.2022.9919928

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers developed a reinforcement learning-based approach to make optimal routing decisions while satisfying security requirements [28]. In the proposed method, each state represents a switch on the data plane of the Software-Defined Network.…”

Section: Applications In the Network Environmentmentioning

confidence: 99%

Anonymous Traffic Detection Based on Feature Engineering and Reinforcement Learning

Liu,

Park

2024

Sensors

View full text Add to dashboard Cite

Anonymous networks, which aim primarily to protect user identities, have gained prominence as tools for enhancing network security and anonymity. Nonetheless, these networks have become a platform for adversarial affairs and sources of suspicious attack traffic. To defend against unpredictable adversaries on the Internet, detecting anonymous network traffic has emerged as a necessity. Many supervised approaches to identify anonymous traffic have harnessed machine learning strategies. However, many require access to engineered datasets and complex architectures to extract the desired information. Due to the resistance of anonymous network traffic to traffic analysis and the scarcity of publicly available datasets, those approaches may need to improve their training efficiency and achieve a higher performance when it comes to anonymous traffic detection. This study utilizes feature engineering techniques to extract pattern information and rank the feature importance of the static traces of anonymous traffic. To leverage these pattern attributes effectively, we developed a reinforcement learning framework that encompasses four key components: states, actions, rewards, and state transitions. A lightweight system is devised to classify anonymous and non-anonymous network traffic. Subsequently, two fine-tuned thresholds are proposed to substitute the traditional labels in a binary classification system. The system will identify anonymous network traffic without reliance on labeled data. The experimental results underscore that the system can identify anonymous traffic with an accuracy rate exceeding 80% (when based on pattern information).

show abstract

Section: Applications In the Network Environmentmentioning

confidence: 99%