Keynote Speech 1: Integrating Big Data, Data Science and Cyber Security with Applications in Internet of Transportation and Infrastructures

The collection, storage, manipulation, analysis, and retention of massive amounts of data have resulted in new technologies including big data analytics and data science. It is now possible to analyse massive amounts of data and extract useful nuggets. However, the collection and manipulation of this data has also resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. Furthermore, the massive amounts of data being stored may also be vulnerable to cyber attacks. 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 analyse vast amounts of data and therefore privacy enhanced data science techniques are being developed. With the advent of the web, computing systems are now being used in every aspect of our lives from mobile phones to autonomous vehicles. It is now possible to collect, store, manage, and analyse vast amounts of sensor data emanating from numerous devices and sensors including from various transportation systems. Such systems collectively are known as the Internet of Transportation, which is essentially the Internet of Things for Transportation, where multiple autonomous transportation systems are connected through the web and coordinate their activities. However, security and privacy for the Internet of Transportation and the infrastructures that support it is a challenge. Due to the large volumes of heterogenous data being collected from numerous devices, the traditional cyber security techniques such as encryption are not efficient to secure the Internet of Transportation. Some Physics-based solutions being developed are showing promise. More recently, the developments in Data Science are also being examined for securing the Internet of Transportation and its supporting infrastructures. 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 three 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. Second it will discuss the developments on securing the Internet of Transportation and its supporting infrastructures and examine the privacy implications. Finally, it will describe ways in which Big Data, Data Science and Security could be incorporated into the Internet of Transportation and Infrastructures.