RicherPicture: Semi-automated cyber defence using context-aware data analytics

Conference item

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 decisionmaking 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.

Authors: Erola, A; Agrafiotis, I; Happa, J; Goldsmith, M; Creese, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Electrical and Electronics Engineers
• Host title: International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA 2017)
• Journal: International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA 2017)
• Publication date: 2017-10-01
• Acceptance date: 2017-04-12
• DOI: 10.1109/CyberSA.2017.8073399