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. These use cases are part of Cybersecurity Data Mining Competition (CDMC) 2017. The efficient network architecture for DNNs 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 DNNs performed well in comparison to the classical machine learning algorithm 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 DNNs and XGBoost on Android malware classification 0.940 and 0.741, incident detection 1.00 and 0.997, and fraud detection 0.972 and 0.916 respectively. The accuracy obtained by DNNs varies -0.05%, +0.02%, -0.01% from the top scored system in CDMC 2017 tasks. In this era of technical modernization, explosion of new opportunities and efficient potential resources for organizations have emerged but at the same time these technologies have resulted in threats to the economy. In such a scenario proper security measures plays a major role. Now days, hacking has become a common practice in organizations in order to steal data and information. This highlights the need for an efficient system to detect and prevent the fraudulent activities. cyber security is all about the protection of systems, networks and data in the cyberspace.Malware remains one of the maximum enormous security threats on the Internet. Malware are the softwares which indicate malicious activity of the file or programs. These are unwanted programs since they cause harm to the intended use of the system by making it behave in a very different manner than it is supposed to behave. Solutions with Antivirus and blacklists are used as the primary weapons of resistance against these malwares. Both approaches are not effective. This can only be used as an initial shelter in real time malware detection system. This is primarily due to the fact that both approaches completely fails in detecting the new malware that is created using polymorphic, metamorphic, domain flux and IP flux.Machine learning algorithms have played a pivotal role in several use cases of cyber security BID0 . Fortunately, deep learning approaches are prevailing subject in recent days due to the remarkable performance in various long-standing artificial intelligence (AI) supervised and unsupervised challenges BID1 . This paper evaluates the effectiveness of deep neural networks (DNNs) for cyber security use cases: Android malware classification, incident detection and fraud detection.The paper is structured as follows. Section II discusses the related work. Section III discusses the background knowledge of deep neural networks (DNNs). Section IV presents the proposed methodology including the description of the data set. Results are displayed in Section V. Conclusion is placed in Section VI. This paper has evaluated the performance of deep neural networks (DNNs) for cyber security uses cases: Android malware classification, incident detection and fraud detection. Additionally, other classical machine learning classifier is used. In all cases, the performance of DNNs is good in comparison to the classical machine learning classifier. Moreover, the same architecture is able to perform better than the other classical machine learning classifier in all use cases. The reported results of DNNs can be further improved by promoting training or stacking a few more layer to the existing architectures. This will be remained as one of the direction towards the future work.
Deep-Net: Deep Neural Network for Cyber Security Use Cases
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