Abstract
Machine learning has played an important role in the last decade mainly in natural language processing, image processing and speech recognition where it has performed well in comparison to the classical rule based approach. The machine learning approach has been used in cyber security use cases namely, intrusion detection, malware analysis, traffic analysis, spam and phishing detection etc. Recently, the advancement of machine learning typically called as 'deep learning' outperformed humans in several long standing artificial intelligence tasks. Deep learning has the capability to learn optimal feature representation by itself and more robust in an adversarial environment in compared to classical machine learning algorithms. This approach is in early stage in cyber security. In this work, to leverage the application of deep learning architectures towards cyber security, we consider intrusion detection, traffic analysis and Android malware detection. In all the experiments of intrusion detection, deep learning architectures performed well in compared to classical machine learning algorithms. Moreover, deep learning architectures have achieved good performance in traffic analysis and Android malware detection too.