A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://digibug.ugr.es/bitstream/handle/10481/55289/OptScaling_preprint.pdf;jsessionid=C7AE3F4D428129FBDB05609C2625BC27?sequence=1">the original URL</a>. The file type is <code>application/pdf</code>.
Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats
<span title="">2019</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xqa4uhvxwvgsbdpllabnuanz6e" style="color: black;">IEEE Transactions on Information Forensics and Security</a>
</i>
Preserved Fulltext
This paper presents a semi-supervised approach for intrusion detection. The method extends the unsupervised Multivariate Statistical Network Monitoring approach based on Principal Component Analysis by introducing a supervised optimization technique to learn the optimum scaling in the input data. It inherits the advantages of the unsupervised strategy, capable of uncovering new threats, with that of supervised strategies, able of learning the pattern of a targeted threat. The supervised
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tifs.2019.2894358">doi:10.1109/tifs.2019.2894358</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2a7oxvm6d5chvkxqkduvqahife">fatcat:2a7oxvm6d5chvkxqkduvqahife</a>
</span>
more »
... is based on an extension of the gradient descent method based on Partial Least Squares (PLS). Moreover, we enhance this method by using sparse PLS variants. The practical application of the system is demonstrated on a recently published real case study, showing relevant improvements in detection performance and in the interpretation of the attacks.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200307180542/https://digibug.ugr.es/bitstream/handle/10481/55289/OptScaling_preprint.pdf;jsessionid=C7AE3F4D428129FBDB05609C2625BC27?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/50/6e/506e1fe1f6a9ee092a2b441c198aaf255d4c3e80.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tifs.2019.2894358">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
ieee.com
</button>
</a>