Analytics framework for cyber defense using data from multiple sources - Vemulapalli, Hitesh; Gochhait, Saikat (International Journal of Psychosocial Rehabilitation, 2020)
The concept of analytics to improve data security management is the key component for cyber defense against all possible attack vectors. Based on the IT information available and their possible affordances, a research model can be constructed to analyze the mechanism behind analytics usage for better information security management. At the same time, the model takes care of the position of IT convergence and data-driven communities and has been tested empirically using real-time data using a partial least squares structural equation model. The data-driven culture and incorporation of IT processes provide a constructive collaboration impact on the dependencies between business analytics and management of data security. However, in the current IT environment, it becomes necessary to define and forecast the intent of the sophisticated targeted attacks using noisy multisource data (Gochhait, 2011). So we discuss ways to merge this heterogeneous data and perform correlation analysis, which can be used in the proposed analytics framework for better detection and prevention against targeted cyberattacks. The framework also recommends using attack graph analysis and several security metrics to understand the effectiveness of our protection systems. This framework can be extended to cloud technologies as well, enhancing the management of cloud computing data security (Gochhait, Shou & Fazalbhoy, 2020). The key to creating a successful framework using analytics is not the amount of data but mining that generates insights. Thus, from the perspective of cloud computing, analytics support decision rationality affordance through the decision-making affordance for better security management practices.