In the context of increasing cyber threats and stringent regulations, this article presents a data security approach based on multi-level labeling to track data. By classifying data sensitivity levels and labeling them, our method enables tracking data throughout its lifecycle, from collection to destruction. Integrating technologies like machine learning enhances this traceability, allowing real-time tracking and risk anticipation. Our model demonstrates superior performance compared to existing studies in terms of precision and reliability, while ensuring compliance with international standards.

In the big data era, the cloud computing services have been adopted to face the emergence of data that needs to be stored and processed properly. However, these services need to provide safety mechanisms to insure its secure adoption. Thus, several solutions have been proposed including the use of secure architectures by customers. In that context, an architecture based on multi-agent systems has been proposed which aims to secure both storage and exploration of data hosted in the Cloud. In this paper, we present a brief synthesis of data security methods. We then focus on the multi-agent system architecture. Finally, we propose our solution considering the design and implementation in Java of an alteration agent which will ensure the secure storage of data stored in the Cloud. We finally present the test results of this agent on real datasets.

Cloud computing services have been adopted to provide the necessary tools and resources to face the emergence of data that needs to be stored and processed properly. However, these promising services, raise the issue of security and reliability in terms of data confidentiality, control and loss of intellectual property. In this work, we exploit the characteristics of multi agent systems to deliver an optimal and secure solution for data storage and exploration in the Cloud. Our solution is based on an encryption process before storage, while an intelligent multi agent system was designed and simulated to optimize the exploration of the data in a Cloud environment. Our architecture aims to use adaptive agents able to predict alerts, make decisions and block any intrusion.

Cloud business intelligence is an increasingly popular choice to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. In this paper, we propose a novel approach for securing cloud data warehouses by flexible verifiable secret sharing, fVSS. Secret sharing encrypts and distributes data over several cloud service providers, thus enforcing data privacy and availability. fVSS addresses four shortcomings in existing secret sharing-based approaches. First, it allows refreshing the data warehouse when some service providers fail. Second, it allows on-line analysis processing. Third, it enforces data integrity with the help of both inner and outer signatures. Fourth, it helps users control the cost of cloud warehousing by balancing the load among service providers with respect to their pricing policies. To illustrate fVSS' efficiency, we thoroughly compare it with existing secret sharing-based approaches with respect to security features, querying power and data storage and computing costs.

The dramatic proliferation of sophisticated cyber attacks, in conjunction with the ever growing use of Internet-based services and applications, is nowadays becoming a great concern in any organization. Among many efficient security solutions proposed in the literature to deal with this evolving threat, ensemble approaches, a particular family of data mining, have proven very successful in designing high performance intrusion detection systems (IDSs) resting on the mutual combination of multiple classifiers. However, the strength of ensemble systems depends heavily on the methods to generate and combine individual classifiers. In this thread, we propose a novel design method to generate a robust ensemble-based IDS. In our approach, individual classifiers are built using both the input feature space and additional features exploited from k-means clustering. In addition, the ensemble combination is calculated based on the classification ability of classifiers on different local data regions defined in form of k-means clustering. Experimental results prove that our solution is superior to several well-known methods.