Detection of Distributed Denial of Service Attacks Using Artificial Neural Networks

Abdullah Aljumah
<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> &nbsp;
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
more &raquo; ... 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.
<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>
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