A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit <a rel="external noopener" href="http://thesai.org/Downloads/Volume8No8/Paper_41-Detection_of_Distributed_Denial_of_Service_Attacks.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Detection of Distributed Denial of Service Attacks Using Artificial Neural Networks
<span title="">2017</span>
<i title="The Science and Information Organization">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a>
</i>
Preserved Fulltext
Distributed Denial of Services (DDoS) is a ruthless attack that targets a node or a medium with its false packets to decline the network performance and its resources. Neural networks is a powerful tool to defend a network from this attack as in our proposed solution a mitigation process is invoked when an attack is detected by the detection system using the known patters which separate the legitimate traffic from malicious traffic that were given to artificial neural networks during its
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2017.080841">doi:10.14569/ijacsa.2017.080841</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aj5vol6llvffdpuk23pb2t73ru">fatcat:aj5vol6llvffdpuk23pb2t73ru</a>
</span>
more »
... g process. In this research article, we have proposed a DDoS detection system using artificial neural networks that will flag (mark) malicious and genuine data traffic and will save network from losing performance. We have compared and evaluated our proposed system on the basis of precision, sensitivity and accuracy with the existing models of the related work.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720101714/http://thesai.org/Downloads/Volume8No8/Paper_41-Detection_of_Distributed_Denial_of_Service_Attacks.pdf" 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/0f/ad/0fadc700a80e4b23ac2e543acd455273007d52fc.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2017.080841">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="unlock alternate icon" style="background-color: #fb971f;"></i>
Publisher / doi.org
</button>
</a>