ACoPE: An adaptive semi-supervised learning approach for complex-policy enforcement in high-bandwidth networks

摘要

Today's high-bandwidth networks require adaptive analyzing approaches to recognize the network variable behaviors. The analyzing approaches should be robust against the tack of prior knowledge and provide data to impose more complex policies. In this paper, ACoPE is proposed as an adaptive semi-supervised learning approach for complex-policy enforcement in high-bandwidth networks. ACoPE detects and maintains inter-flows relationships to impose complex-policies. It employs a statistical process control technique to monitor accuracy. Whenever the accuracy decreased, ACoPE considers it as a changed behavior and uses data from a deep packet inspection module to adapt itself with the change.The performance of ACoPE in analyzing network traffic is evaluated through UNB ISCX VPN-nonVPN and UNB ISCX Tor-nonTor datasets. The performance is compared with 10 different stream and traditional classification algorithms. ACoPE outperforms the stream classifiers, with 95.92% accuracy, 86.21% precision, and 73.29% recall in VPN dataset, and with 81.12% accuracy, 73.59% precision, and 61.08% recall in Tor dataset. The effectiveness of ACoPE to address the main constraints in analyzing of high-bandwidth networks to enforce security policies, namely comprehensive processing and adaptive learning, are confirmed through three different scenarios. Efficiency and accuracy of ACoPE in real high-bandwidth networks are evaluated by a pilot study, which indicates its efficiency and accuracy in analyzing high-bandwidth networks. (C) 2019 Elsevier B.V. All rights reserved.