Nimble Cybersecurity Incident Management through Visualization and Defensible Recommendations

ABSTRACT:

Analysts engaged in real-time monitoring of cybersecurity incidents must quickly and accurately respond to alerts generated by intrusion detection systems. We investigated two complementary approaches to improving analyst performance on this vigilance task: a graph-based visualization of correlated IDS output and defensible recommendations based on machine learning from historical analyst behavior. We tested our approach with 18 professional cybersecurity analysts using a prototype environment in which we compared the visualization with a conventional tabular display, and the defensible recommendations with limited or no recommendations. Quantitative results showed improved analyst accuracy with the visual display and the defensible recommendations. Additional qualitative data from a “talk aloud” protocol illustrated the role of displays and recommendations in analysts’ decision-making process. Implications for the design of future online analysis environments are discussed.