Biblio

Smart technologies at hand have facilitated generation and collection of huge volumes of data, on daily basis. It involves highly sensitive and diverse data like personal, organisational, environment, energy, transport and economic data. Data Analytics provide solution for various issues being faced by smart cities like crisis response, disaster resilience, emergence management, smart traffic management system etc.; it requires distribution of sensitive data among various entities within or outside the smart city,. Sharing of sensitive data creates a need for efficient usage of smart city data to provide smart applications and utility to the end users in a trustworthy and safe mode. This shared sensitive data if get leaked as a consequence can cause damage and severe risk to the city's resources. Fortification of critical data from unofficial disclosure is biggest issue for success of any project. Data Leakage Detection provides a set of tools and technology that can efficiently resolves the concerns related to smart city critical data. The paper, showcase an approach to detect the leakage which is caused intentionally or unintentionally. The model represents allotment of data objects between diverse agents using Bigraph. The objective is to make critical data secure by revealing the guilty agent who caused the data leakage.

In a continually evolving cyber-threat landscape, the detection and prevention of cyber attacks has become a complex task. Technological developments have led organisations to digitise the majority of their operations. This practice, however, has its perils, since cybespace offers a new attack-surface. Institutions which are tasked to protect organisations from these threats utilise mainly network data and their incident response strategy remains oblivious to the needs of the organisation when it comes to protecting operational aspects. This paper presents a system able to combine threat intelligence data, attack-trend data and organisational data (along with other data sources available) in order to achieve automated network-defence actions. Our approach combines machine learning, visual analytics and information from business processes to guide through a decision-making process for a Security Operation Centre environment. We test our system on two synthetic scenarios and show that correlating network data with non-network data for automated network defences is possible and worth investigating further.