  Deep neural networks (DNNs) have witnessed as a powerful approach in this
year by solving long-standing Artificial intelligence (AI) supervised and
unsupervised tasks exists in natural language processing, speech processing,
computer vision and others. In this paper, we attempt to apply DNNs on three
different cyber security use cases: Android malware classification, incident
detection and fraud detection. The data set of each use case contains real
known benign and malicious activities samples. The efficient network
architecture for DNN is chosen by conducting various trails of experiments for
network parameters and network structures. The experiments of such chosen
efficient configurations of DNNs are run up to 1000 epochs with learning rate
set in the range [0.01-0.5]. Experiments of DNN performed well in comparison to
the classical machine learning algorithms in all cases of experiments of cyber
security use cases. This is due to the fact that DNNs implicitly extract and
build better features, identifies the characteristics of the data that lead to
better accuracy. The best accuracy obtained by DNN and XGBoost on Android
malware classification 0.940 and 0.741, incident detection 1.00 and 0.997 fraud
detection 0.972 and 0.916 respectively.
