DDoS Detection Using Machine Learning Ensemble

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Arpit Kumar Jain, et. al.


In the era of internet and online connectedness, where data is the most valuable asset, it is ever important for an organization to protect itself and it’s assets from various security threats. One of these threats is a Distributed Denial of Service (DDoS) attack that can cut off the network service by overwhelming the targeted server or network by flooding it with superfluous requests in an attempt to overload the server to prevent legitimate requests from being fulfilled. DDoS attacks utilize multiple compromised systems as sources of internet traffic to increase their effectiveness. What makes DDoS attacks more lethal is that fighting them requires differentiating legitimate requests from illegitimate ones. A site or service unexpectedly being sluggish or

inaccessible is the most obvious symptom of a DDoS attack. But since a number of causes like legitimate spike

in network traffic can create similar issues , further investigation is necessary.)


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How to Cite
et. al., A. K. J. . (2021). DDoS Detection Using Machine Learning Ensemble . Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 1647–1655. https://doi.org/10.17762/turcomat.v12i12.7666