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In the literature, machine learning algorithms are
frequently used in detecting anomalies in network traffic and in
building intrusion detection systems. Deep learning is a subfield of
machine learning that trains a computer-based system to perform
humanitarian tasks, such as disease diagnosis, speech recognition,
image recognition, fraud detection, and making predictions. In the
experimental study, NSL-KDD dataset was used for evaluating the
performance of the proposed deep learning based DDoS detection
model. NLS-KDD dataset contains normal network traffic and 23
different DDoS attacks that consists of 41 features. In the
experimental study two different experiments are carried out.
Firstly, the proposed deep neural network detected the Dos attacks
with 0.988 classification accuracy. In the second experiment, the
number of features of NSL-KDD is reduced to 24 by examining the
previous feature reduction research on NSL-KDD dataset. The
proposed deep neural network classified the all cyber-attacks with
0.984 classification accuracy. The 10-fold cross validation is used
for all experiments. As a result, the proposed deep learning based
DDoS detection achieved good performance.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Ayşegül Sungur Ünal
Mehmet Hacibeyoglu