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IJSTR >> Volume 10 - Issue 4, April 2021 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Distributed Denial Of Service Attack Detection Based On Neural Network: A Comparative Study

[Full Text]

 

AUTHOR(S)

Roheen Qamar, Aijaz Ahmed Arain, Kelash Kanwar, Fida Hussain Khoso, Fareed Jokhio

 

KEYWORDS

Knowledge Discovery Dataset (KDD), Artificial Neural Network (ANN), Distributed Denial of Service (DDoS) Attacks, Broyden-Fletcher-Goldarb-Shanno (BFGS), One step secant method.

 

ABSTRACT

This research study analyzes machine learning-based techniques to identify Distributed Denial of Service (DDoS) attacks. Cyber-attacks are used to bring down the services of affected servers. Hereafter these servers cannot provide services to the end-users. Therefore, it is essential to detect and mitigate these types of attacks. Machine learning-based techniques can differentiate between attacks and legitimate user requests. Moreover, different types of attacks can be classified by these techniques. In this research, three different neural networks have been compared, these are (i) Feed Forward Neural Network (ii) Case Cade Neural Network and (iii) Fitting Neural Network. These networks have been trained with two different training algorithms, i.e., Quasi-newton backpropagation algorithm and one step secant algorithm. During this research work, the knowledge discovery data set KDD-CUP99 is used. The results indicate that fit net shallow neural network has better accuracy result with a short training time.

 

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