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oday, almost everything is done through networks.
Especially, Networks are widely used for transportation of data.
Various methods are used to move the data from one place to
another. One of these methods is Optical Burst Switching (OBS).
When carrying data in OBS, some of the threats may be
encountered as a result of security shortcomings. Some of these
threats are Spoofing, Replay Attack, Circulating Burst Header
Attack and Burst Header Packet (BHP) Flooding Attack.
Detection of threats is difficult but it is very important to our
safety. Therefore, using Machine Learning (ML) methods to
detect threats will give us flexibility, time and accuracy. In this
study, we will classify BHP Flooding Attack data that have four
class labels with ML methods. Our class labels are as follows:
Misbehaving-Block (Block), Behaving-No Block (No Block),
Misbehaving-No Block (NB-No Block), and Misbehaving-Wait
(NB-Wait). Methods used in classification are Decision Tree
(J48), Logistic, Multilayer Perceptron (MLP), Random Tree
(RT), Reduce Error Pruning (REP) Tree and Naive Bayes (NB).
Since there are 22 properties in the data set, the results of feature
selection are also examined using the same classification methods.
As a result, J48 and RT have been found to achieve the best
results with 100% accuracy.
International Conference on Cyber Security and Computer Science
ICONCS
V.N. UZEL
E. SARAÇ EŞSİZ