مطالب

Background: The conceptualization of Moroccan cyberspace is intrinsically linked to the country's international and regional dynamics, in particular those of its African and Mediterranean neighborhood.Methodology: we have mobilized quantitative techniques and potential comparative analysis to address our crucial research questions around the strengths and weaknesses within Morocco's national cyber strategy by analyzing and comparing them with other international experiences, and finally recommending best practices to maintain the regional leadership position.Discussion: Although Morocco has acquired considerable expertise in comparison with this environment, regarding proven mechanisms of cyber stability, and the institutionalization and organization of national cyber security. However, one of the main obstacles lies in the centralization of these practices by the State, as well as in the sectorial and selective approach chosen by Moroccan policymakers to secure the national cyber space. This strategy, contrary to the country's principles of openness, was developed on the basis of the arguments, based on the priorities and strategic challenges specific to each sector.Aims: We open up our local experience to comparison with other known global experiences in the field of national cybersecurity, to decipher the strengths and weaknesses of national digital strategies and to fill the gaps causing the weakening of our replicating processes.Conclusion: The question is whether a simple reform of the central state's public policies on national cybersecurity is effective or, on the contrary, the situation requires a drastic and broader intervention, materialized by major investments and structural reforms in entire development strategies.

The widespread adoption of Android phones has heightened concerns about user privacy. This research presents an Adaptive Privacy Management System (APMS) that integrates Machine Learning (ML) models with Differential Privacy techniques to enhance privacy protection. The APMS monitors application behavior and employs ML algorithms to detect anomalies and enable context-aware privacy enforcement. Differential Privacy ensures that sensitive data remains protected through the addition of noise and privacy-preserving computations. Experimental results demonstrate that the APMS achieves a 92.5% accuracy rate in detecting the privacy leakage. The anomaly detection model, using Random Forest, shows high accuracy (92.5%), recall (89.5%), and precision (73.9%), effectively identifying both normal and anomalous behaviors. Additionally, the impact of noise on data utility, controlled by the privacy budget (ε), is manageable. The results show that APMS is a robust system for safeguarding user confidential information, contributing to a more secure and privacy-centric Android ecosystem.