| Abstract
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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.
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| Author
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Eduardo Viegas +
, Altair Santin +
, Vinicius Vielmo Cogo +
, Vilmar Abreu +
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| Booktitle
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Proceedings of the 2020 IEEE International Conference on Communications (ICC) +
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| Document
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Document for Publication-Viegas2020semisupervised.pdf +
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| Key
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Viegas2020semisupervised +
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| Month
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jun +
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| NumPubDate
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2,020.06 +
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| ResearchLine
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Fault and Intrusion Tolerance in Open Distributed Systems (FIT) +
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| Title
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A Reliable Semi-Supervised Intrusion Detection Model: One Year of Network Traffic Anomalies +
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| Type
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inproceedings +
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| Year
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2020 +
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| Has improper value forThis property is a special property in this wiki.
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Url +
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| Categories |
Publication +
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| Modification dateThis property is a special property in this wiki.
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28 February 2020 18:49:57 +
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