Integrating Big Data, Data Science and Cyber Security with Applications in Cloud and Internet of Things

Abstract

Big Data, Data Science and Security are being integrated to solve many of the security and privacy challenges. For example, machine learning techniques are being applied to solve security problems such as malware analysis and insider threat detection. However, there is also a major concern that the machine learning techniques themselves could be attacked. Therefore, the machine learning techniques are being adapted to handle adversarial attacks. This area is known as adversarial machine learning. In addition, privacy of the individuals is also being violated through these machine learning techniques as it is now possible to gather and analyze vast amounts of data and therefore privacy enhanced data science techniques are being developed.To assess the developments on the integration of Big Data, Data Science and Security over the past decade and apply them to the Internet of Transportation, the presentation will focus on four aspects. First it will examine the developments on applying Data Science techniques for detecting cyber security problems such as insider threat detection as well as the advances in adversarial machine learning. Some developments on privacy aware and policy-based data management frameworks will also be discussed. Next it will discuss how cloud technologies may be used to securely and privately share the information for various Big Data applications such as the Internet of Things. Finally, it will describe ways in which Big Data, Data Science and Security could be incorporated into these applications.