5G brings significant advancement, offering lower latency, and improved connectivity. Yet, its complexity, stemming from factors such as integrating advanced technologies like Software Defined Networking (SDN) and slicing, introduces challenges in implementing strong security measures against emerging threats. Although Intrusion Detection Systems (IDSs) can successfully detect attacks, the novelty of 5G creates an expanded attack surface. Collaboration is essential for detecting novel, distributed attacks, and ensuring comprehensive observability in multiparty networks. However, such collaboration raises privacy concerns due to the sensitivity of shared data. Federated Learning (FL), a collaborative Machine Learning (ML) approach, is a promising solution to preserve privacy as the model is trained locally without exchanging raw data.

International audience

International audience

Recent evolutions in cloud infrastructures allowed service providers to tailor new services for demanding customers. Providing these services confronts the infrastructure providers with costs and constraints considerations. In particular, security constraints are a major concern for today's businesses as the leak of personal information would tarnish their reputation. Recent works provide examples on how an attacker may leverage the infrastructure's weaknesses to steal sensitive information from the users. Specifically, an attacker can leverage maintenance processes inside the infrastructure to conduct an attack. In this paper, we consider the migration of a virtual network as the maintenance process. Then we determine the optimal monitoring resources allocation in this context with a Markov Decision Process. This model takes into account the impact of monitoring the infrastructure, the migration process and finally how the attacker may chose particular targets in the infrastructure. We provide a working prototype implemented in Python1.

E-Assessment is an innovative form for the evaluation of learners' knowledge and skills in online education, as well as in blended-learning environments, where part of the assessment activities is carried out online. As e-assessment involves online communication channel between learners and educators, as well as data transfer and storage, security measures are required to protect the environment against system and network attacks. The issue concerning security is challenging from both educational and technical point of views. Such issues are discussed under the scope of the TeSLA project. Educational challenging problems at e-assessment are analyzed and technical architectural recommendations for securing the e-assessment system according to the General Data Protection Regulation are provided.

Network virtualization provides a flexible solution to reduce costs, share network resources and improve recovery time upon failure. An important part of virtual network management consists in migrating them in order to optimize resource allocation and react to link failures. However, the migration process might entail the loss of security properties in the virtual network, such as confidentiality. In this paper, we present the first approach combining formal models and virtualization to prove confidentiality preservation during the migration process. We describe the network environment, the migration process and the confidentiality with a set of logical predicates that will be used by SNARK to obtain the formal proof of the preservation. We validate our theoretical approach by exhibiting confidentiality violation detection on an illustrative use case.

The new developments in cloud computing have introduced significant security challenges to guarantee the confidentiality, integrity, and availability of outsourced data. A service level agreement (SLA) is usually signed between the cloud provider (CP) and the customer. For redundancy purposes, it is important to verify the CP's compliance with data backup requirements in the SLA. There exist a number of security mechanisms to check the integrity and availability of outsourced data. This task can be performed by the customer or be delegated to an independent entity that we will refer to as the verifier. However, checking the availability of data introduces extra costs, which can discourage the customer of performing data verification too often. The interaction between the verifier and the CP can be captured using game theory in order to find an optimal data verification strategy. In this paper, we formulate this problem as a two player non-cooperative game. We consider the case in which each type of data is replicated a number of times, which can depend on a set of parameters including, among others, its size and sensitivity. We analyze the strategies of the CP and the verifier at the Nash equilibrium and derive the expected behavior of both the players. Finally, we validate our model numerically on a case study and explain how we evaluate the parameters in the model.

This demonstration shows a new concept for securely downloading keys in RFID devices, with an Android NFC enabled mobile. Today most of electronic ticketing or physical access control systems work with Mifare components. A dual interface RFID is compatible with this deployed ecosystem but also includes trusted computing facilities, and is compliant with the ISO 14443 standard. We use such a device, running a trusted SSL/TLS stack, in order to perform HTTPS operations supervised by an Android mobile phone. Keys are securely downloaded from WEB servers and afterward used by legacy Mifare systems, such as electronic locks.