Smart Infrastructure Project Decision-Making Under Cyber Threat Uncertainty Using Hybrid DSS Models

Abstract

With the rise of digitalization in the world, there is a much greater emphasis on the use of smart technologies in infrastructure systems, which has greatly contributed to the improvement of efficiency, service delivery, and data analytics. It is questionable whether this digitalization journey does not expose the critical infrastructure to emerging and unforeseeable cyber threats such as phishing, ransomware, malware, and Distributed Denial of Service (DDoS) attacks. These threats are uncertain and very complex; hence, their traditional decision-making techniques do not have flexibility in dealing with the ambiguous, incomplete, or dynamic intelligence on the threats. This study identifies a hybrid Decision Support System (DSS) model consisting of Natural Language Processing (NLP), fuzzy logic, sentiment analysis and multi-criteria decision making (MCDM) to solve cyber threat uncertainty within a smart infrastructure setting. The research corpus is a structured cybersecurity dataset with an NLP extension with the threat description, keywords extraction, and risk prediction, severity scoring. Using the proposed hybrid approach, the DSS framework can categorize threats, identify Indicators of Compromises (IOCs), estimate severity, and propose defense measures based on both structured and unstructured data. e.g., Python, Tableau, or Excel are used as a visualization tool to analyze threat distributions, sentiment scores, and response strategies. In this study, the results obtained dictate that certain types of threats, attack vectors, geographical targeting, and severity of risk exist in good lineage that lends critical value to decisions made during strategy. The threat-driven sentiment analysis on discussions provides yet another contextual dimension that helps to make better and timely cybersecurity planning options. The proposed model illustrates that it is possible to transform the cyber threat uncertainty into actionable intelligence, which would allow the stakeholders to focus on threats-related priorities, to properly invest funds into the procurement of the appropriate resources, and to create resiliency-based responses. The proposed framework combines an intelligent decision-making process with a scalable paradigm of threat dynamics and, therefore, contributes to the growing complex field of cyber-resilient infrastructure. Finally, the hybrid DSS solution presents a viable and novel way of providing cybersecurity posture and safeguarding the critical infrastructure resources against the formidable and enduring cyber threats.