A Robust Information Life Cycle Management Framework for Securing and Governing Critical Infrastructure Systems

Abstract

In modern societies, the rampant growth of the Internet, both on the technological and social level, has created fertile ground for the emergence of new types of risk. On top of that, it enhances pre-existing threats by offering new means for accessing and exploiting Critical Infrastructures. As the kinds of potential threats evolve, the security, safety and resilience of these infrastructures must be updated accordingly, both at a prevention, as well as a real-time confrontation level. Our research approaches the security of these infrastructures with a focus on the data and utilization of every possible piece of information that derives from this ecosystem. Such a task is quite daunting, since the quantity of data that requires processing resides in the Big Dataspace. To address this, we introduce a new well-defined Information Life Cycle in order to properly model and optimise the way information flows through a modern security system. This life cycle covers all the possible stages, starting from the collection phase up until the exploitation of information intelligence. That ensures the efficiency of data processing and filtering while increasing both the veracity and validity of the final outcome. In addition, an agile Framework is introduced that is optimised to take full advantage of the Information Life Cycle. As a result, it exploits the generated knowledge taking the correct sequence of actions that will successfully address possible threats. This Framework leverages every possible data source that could provide vital information to Critical Infrastructures by performing analysis and data fusion being able to cope with data variety and variability. At the same time, it orchestrates the pre-existing processes and resources of these infrastructures. Through rigorous testing, it was found that response time against hazards was dramatically decreased. As a result, this Framework is an ideal candidate for strengthening and shielding the infrastructures’ resilience while improving management of the resources used.

Framework for critical information infrastructure protection in smart government: a case study in Indonesia

“…The governance dimension comprises 11 variables that examine relationships with external parties involved in the service (Limba et al , 2017). Within the regulation element, the variables encompass regulations about security goals, regulations governing security functions and regulations governing risk management (Moulos et al , 2018). The security standards element is represented by two variables, explicitly focusing on compliance with security function standards and adherence to applicable standards (Setiawan et al , 2015; Yamada et al , 2020).…”

Diftong: a tool for validating big data workflows

“…In a more general context, the tool compares any two databases that have the same schemas and highlights the differences and similarities between them. Diftong could be included in a more robust information life cycle management framework as described in [35]. Similarly by ensuring data quality, big data analytics [36] can be applied with more trust [37].…”