Abstract
A distributed denial-of-service (DDoS) attack attempts to prevent people from accessing a server. Awebsite may become inaccessible due to a DDoS attack because the server is inundated with fake requests and cannothandle real ones. A DDoS attack affects a large number of computers. Attackers employ a zombie network, whichis a collection of infected machines on which the attacker has hidden the denial-of-service attacking application tocarry out a DDoS attack. The MATLAB 2018a simulator was used in this study for training. Additionally, duringdesign, the knowledge discovery dataset (KDD) was cleaned and the values of attacks were incorporated. A neuralnetwork model was subsequently developed, and the KDD was trained using a recursive artificial neural network.This network was developed using five distinct training algorithms: 1) Fletcher–Powell conjugate gradient, 2)Polak–Ribiére conjugate gradient of, 3) resilient backpropagation, 4) gradient conjugation with Powell/Beale restarts,and 5) gradient descent algorithm with variable learning rate. The artificial neural network toolset in MATLAB wasused to investigate the detection of DDoS attacks. The conjugate gradient with Powell/Beale restart algorithm had asuccess rate of 99.9% and a training time of 00:53. This inquiry uses the KDD-CUP99 dataset. Has a better level ofaccuracy, according to the results.
Recommended Citation
Qamar, Roheen
(2022)
"Gradient Techniques to Predict Distributed Denial-Of-ServiceAttack,"
Iraqi Journal for Computer Science and Mathematics: Vol. 3:
Iss.
2, Article 6.
DOI: https://doi.org/10.52866/ijcsm.2022.02.01.006
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol3/iss2/6
