The number of new vulnerabilities continues to rise significantly each year. Simultaneously, vulnerability databases have challenges in promptly sharing new security events with enough information to improve protections against emerging cyberattack vectors and possible exploits. In this context, several organizations adopt strategies to protect their data, technologies, and infrastructures from cyberattacks by implementing anticipatory and proactive approaches to their system security activities. To this end, vulnerability management systems play a crucial role in mitigating the impact of cyberattacks by identifying potential vulnerabilities within an organization and alerting cyber teams. However, the effectiveness of these systems, which employ multiple methods and techniques to identify weaknesses, relies heavily on the accuracy of published security events. For this reason, we introduce a discussion concerning existing vulnerability detection methods through an in-depth literature study of several research papers. Based on the results, this paper points out some issues related to vulnerability databases handling that impact the effectiveness of certain vulnerability identification methods. Furthermore, after summarizing the existing methodologies, this study classifies them into four approaches and discusses the challenges, findings, and potential research directions.

With the purpose of managing efficiency in a large number of published vulnerabilities, the time-intensive process requires significant effort and efficient vulnerability prioritization procedures. In order to enhance the cybersecurity defense posture, it becomes imperative to ensure alignment with risk tolerance and business needs, making vulnerability management critical for reducing cybersecurity risks, avoiding system breaches, and optimizing organizational resources. However, traditional methods often struggle with accuracy due to static datasets and inconsistent scoring methodologies, leading to challenges in prioritizing vulnerabilities and exposing systems to significant risks. This study aims to propose a novel approach for predicting the exploitability of vulnerabilities using dynamic, continuously updated datasets and advanced learning models. A novel exploitability scoring equation is introduced and rigorously assessed through four methods: the Analytic Hierarchy Process, regression analysis, and two supervised learning models. The methodology incorporates machine learning and deep learning models to compute and predict exploitability scores, following the initial data processing and scoring calculation steps. The research framework integrates data ingestion, cleaning, and correlation processes to ensure data accuracy. It employs a dynamic dataset that is continuously updated with enriched data from diverse sources such as the National Vulnerability Database, ExploitDB, MITRE ATT&CK, and Vulners. Experimental results demonstrate a significant improvement in model training, with an accuracy of 84%. Furthermore, the predictive model achieved 82.9% accuracy in classifying vulnerabilities as exploitable or not, highlighting the importance of dynamic datasets and the comprehensive framework used to enhance vulnerability.

In last decades, the notion of collaborative systems is presented as the new concept in Critical Infrastructures. It regroups different organizations collaborating in order to ensure the continuity of these infrastructures. However, some malicious behavior can arise causing perturbations on the information systems of the organizations. To avoid this behavior, building trust between collaborating organizations is necessary; it allows cooperating and sharing resources in a secure way. In this paper, we present Tr-OrBAC: a new trust model applicable in collaborative systems; it is based on the OrBAC (Organization Based Access Control) access control model and trust evaluation. In this paper, we will focus on explaining and detailing the proposed trust model.

Due to physical and logical vulnerabilities, a critical infrastructure (CI) can encounter failures of various degrees of severity, and since there are many interdependencies between CIs, simple failures can have dramatic consequences on the whole infrastructure. In this paper, we mainly focus on malicious threats that might affect the communication and information systems (the critical information infrastructure, or CII) dedicated to critical infrastructures. We define a new collaborative access control framework called PolyOrBAC, to address the security problems that are specific of CIIs. This approach offers each organisation taking part in the CII the capacity of collaborating with the other ones, while maintaining a control on its resources and on its internal security policy. The approach is demonstrated on a practical scenario, based on real emergency actions in an electric power grid infrastructure.

Due to the necessity of Critical Infrastructure (CI) Protection against different threats, several security policies must be applied among the organizations of CI. Based on our conducted study about the different constraints and requirements of the collaborative systems within CI, we reached a security solution: Tr-OrBAC. Its principle is to evaluate the trustworthiness of collaborating organizations based on relevant trust criteria aimed at enhancing collaboration decision-making. The taken decision presents the attribution of the access to the desired service based on calculated trust score which is the result of the combination of the trust criteria values. Generally, the desired services do not meet the same criticism, security requirements, sensitivity, etc. Also, the collaboration context varies from a set of collaborating organizations to another. In this sense, the importance of each trust criterion depends on the desired service and the collaboration context. In this paper, we focus on detailing the trust criteria used in our approach for collaborative system security. Then, we analyze the context variability with the trust evaluation process. In addition, we present a case study to demonstrate and illustrate the feasibility of our solution for CI protection, especially the electrical grid.

In the collaborative systems, in order to establish successful and protected collaboration, the trust management is first established between the participating entities and the trust score of each entity is calculated. This score is obtained based on the three trust criteria (satisfaction, reputation and recommendation) that are redefined and reused in our model: Tr-OrBAC. The evaluation of these criteria permits to select and choose the appropriate type of access to be assigned to a requester entity in the collaborative system according to its reliability. Each access type will be presented by OrBAC security rules in which we add the trust score as a new variable. The main goal of this paper is to exploit the AHP (Analytic Hierarchy Process) method for multi-criteria analyses in order to evaluate the trust criteria and to select the correct access type for the collaborating entities. In order to illustrate the feasibility of using AHP in Tr-OrBAC for taking the collaboration decision, we apply this solution in the Electrical Grid as a Critical Infrastructure case study.