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Abstract Despite the promising results of machine l Despite the promising results of machine learning for network-based intrusion detection, current techniques are not widely deployed in real-world environments. In general, proposed detection models quickly become obsolete, thus, generating unreliable classifications over time. In this paper, we propose a new reliable model for semi-supervised intrusion detection that uses a verification technique to provide reliable classifications over time, even in the absence of model updates. Additionally, we cope with this verification technique with semi-supervised learning to autonomously update the underlying machine learning models without human assistance. Our experiments consider a full year of real network traffic and demonstrate that our solution maintains the accuracy rate over time without model updates while rejecting only 10.6% of instances on average. Moreover, when autonomous (non-human-assisted) model updates are performed, the average rejection rate drops to just 3.2% without affecting the accuracy of our solution. ut affecting the accuracy of our solution.
Author Eduardo Viegas + , Altair Santin + , Vinicius Vielmo Cogo + , Vilmar Abreu +
Booktitle Proceedings of the 2020 IEEE International Conference on Communications (ICC)  +
Document Document for Publication-Viegas2020semisupervised.pdf +
Key Viegas2020semisupervised  +
Month jun  +
NumPubDate 2,020.06  +
ResearchLine Fault and Intrusion Tolerance in Open Distributed Systems (FIT) +
Title A Reliable Semi-Supervised Intrusion Detection Model: One Year of Network Traffic Anomalies  +
Type inproceedings  +
Year 2020  +
Has improper value forThis property is a special property in this wiki. Url  +
Categories Publication  +
Modification dateThis property is a special property in this wiki. 28 February 2020 18:49:57  +
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