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International audience; Multi-access Edge Computing (MEC) technology has emerged to overcome traditional cloud computing limitations, challenged by the new 5G services with heavy and heterogeneous requirements on both latency and bandwidth. In this work, we tackle the problem of clustering access points in MEC environments, introducing a set of clustering models to be deployed at the pre-provisioning phase. We go through extensive simulations on real-world traffic demands to evaluate the performance of the proposed solutions. In addition, we show how MEC hosts capacity violation can be decreased when integrating access points clustering into the orchestration model, by investigating on solution accuracy when applied on heldout users traffic demands. The obtained results show that our approach outperforms two state-of-the-art algorithms, reducing both memory usage and execution time, by 46% and 50%, respectively, in comparison to a baseline algorithm. It surpasses the two methods in gaining control over MEC hosts capacity usage for different maximum achieved occupancy levels on MEC hosts.

Nowadays, security is becoming a major issue and concern. More and more organizations like hospitals, metropolis or banks are under cyberattacks and have to improve their network infrastructure security. The first prerequisites are to authenticate users, to provide identity and to grant just the needed and useful accesses. These requirements can be solved by implementing a Single Sign-On (SSO) solution. It is an authentication scheme that permits a user to log in with a single identity to any of several related, yet independent, systems. It allows users to log in once and to access services without authenticating again. SSO solutions are classified depending on Authentication, Authorization, and Accounting features. The 'AAA' acronym defines a framework for intelligently controlling access to resources, enforcing security policies, auditing usage, and providing the information necessary to bill for services. These combined processes are considered important for effective network management and cybersecurity. LemonLDAP::NG (LL::NG) is a full AAA WebSSO solution. It implements all standard authentication and identity federation (IdF) protocols. The main LL::NG's advantages compared to other products are its plug-in engine and its advanced handlerbased protection mechanism that can be employed to protect Server2Server exchanges or to offer the SSO as a Service, a solution to implement a full DevOps architecture. LL::NG is a community and professional project mainly employed by the French government to secure Police, Finance or Justice Ministries and a French mobile operator IT infrastructures since 2010. But for several years, contributions come from all around the world and LL::NG is becoming more and more popular.

International audience; Nowadays, botnet-based attacks are the most prevalent cyber-threats type. It is therefore essential to detect this kind of malware using efficient bots detection techniques. This paper presents our security anomalies detection system, based on a model that we named Combined Forest. Our approach consists of merging some pre-processed Decision Trees to highlight different kinds of botnet by detecting their intrinsic exchanges. Using a supervised data approach, each tree is built from a labelled dataset. In order to achieve this, we aggregate the IP-flows into Traffic-flows to extract key features and avoid over-fitting. Then, we tested different machine learning algorithms and selected the most suitable one. After that, many experiments have been done to determine the best parameters and design the most accurate, adaptative and efficient model.