  Machine learning techniques help to understand patterns of a dataset to
create a defense mechanism against cyber attacks. However, it is difficult to
construct a theoretical model due to the imbalances in the dataset for
discriminating attacks from the overall dataset. Multilayer Perceptron (MLP)
technique will provide improvement in accuracy and increase the performance of
detecting the attack and benign data from a balanced dataset. We have worked on
the UGR'16 dataset publicly available for this work. Data wrangling has been
done due to prepare test set from in the original set. We fed the neural
network classifier larger input to the neural network in an increasing manner
(i.e. 10000, 50000, 1 million) to see the distribution of features over the
accuracy. We have implemented a GAN model that can produce samples of different
attack labels (e.g. blacklist, anomaly spam, ssh scan). We have been able to
generate as many samples as necessary based on the data sample we have taken
from the UGR'16. We have tested the accuracy of our model with the imbalance
dataset initially and then with the increasing the attack samples and found
improvement of classification performance for the latter.
