Khalid Bennouk

The emergence of threats and their potential impact obliges organizations to be aligned with standards and frameworks to reduce and anticipate cyber risks. The ISO/IEC 27001 standard offers a set of measures to enhance an organization's cybersecurity posture by deploying an Information Security Management System (ISMS). To meet the conformity with the European Network and Information Systems (NIS2) Directive, entities already adhering to various international standards must map existing security controls with the new NIS2 mandates. To address these challenges, this study proposes a four-phase approach as a new mapping guide to reach compliance, ensuring harmonization of security across European member states, while fostering coordination and information sharing regarding cyber threats. Moreover, the chapter provides a comprehensive analysis of existing ISMS and the requirements outlined in the NIS2 Directive. Finally, the paper presents a side-by-side comparison of articles, recitals, clauses, and obligations, identifying key similarities and clarifying their meaning.

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.

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.