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Computer networks have several issues such as insertion attacks, denial of service attacks, traffic jamming, and unauthorized access. Due to these issues network security is most important. In a network, Distributed Denial of Service (DDoS) attacks may cause significant degradation of the performance of any application. It is very challenging to detect such attacks and undetected attacks are considered as a threat. This paper describes comparative analysis of Denial of Service attacks detection using Feed Forward Neural Networks and Autoencoders which are machine learning based approaches and are usually used for feature learning.
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