Machine Learning (ML) has been widely used in network security monitoring. Although, its application to data intensive use cases and those requiring ultra-low latency remains challenging. This is due to the large amounts of network data and the need of transferring data to a central location hosting analysis services. In this paper, we present a framework to perform in-network analysis by offloading ML inference tasks from end servers to P4-capable programmable network devices. This helps reduce transfer latency and, thus, allows faster attack detection and mitigation. It also improves privacy since the data is processed at the networking devices. The paper also presents an experimental use-case of the framework to classify network traffic, and to early detect and rapidly mitigate against IoT malicious traffic.

5G networks become increasingly pervasive, ensuring the robustness and integrity of network functions. The adoption of HTTP/2 in 5G core functions brings notable performance benefits but also introduces potential security risks. By analyzing HTTP/2 related threats, this research aims to shed light on the security challenges faced by 5G networks. The paper proposes effective security testing methodologies using an open-source solution called 5Greplay to detect these security breaches, enabling network operators to protect against potential attacks, safeguard user privacy, and ensure uninterrupted service continuity. By addressing the specific concerns of HTTP/2 related threats, this research contributes to the overall security posture of 5G network functions and provides valuable insights for the secure deployment of 5G networks in an evolving threat landscape.

The Low-Latency Low-Loss Scalable throughput (L4S) architecture has recently been proposed to reduce the network latency of low-latency services and to allow their flows to coexist with classic ones in the same domain. This coexistence implies monitoring and security challenges. However current monitoring methods, primarily based-on sampling and polling, exhibit performance and granularity limitations. This paper describes the challenges for monitoring LL services and details our solution when introducing a fine-grained and real-time monitoring capability in our P4-based L4S implementation using In-band Network Telemetry. The initial experimental evaluation shows that our solution is able to monitor the metrics of an L4S switch with very few networking and processing overhead and without disturbing the L4S behaviour.

International audience

The fifth generation of mobile broadband is more than just an evolution to provide more mobile bandwidth, massive machine-type communications, and ultra-reliable and low-latency communications. It relies on a complex, dynamic and heterogeneous environment that implies addressing numerous testing and security challenges. In this paper we present 5Greplay, an open-source 5G network traffic fuzzer that enables the evaluation of 5G components by replaying and modifying 5G network traffic by creating and injecting network scenarios into a target that can be a 5G core service (e.g., AMF, SMF) or a RAN network (e.g., gNodeB). The tool provides the ability to alter network packets online or offline in both control and data planes in a very flexible manner. The experimental evaluation conducted against open-source based 5G platforms, showed that the target services accept traffic being altered by the tool, and that it can reach up to 9.56 Gbps using only 1 processor core to replay 5G traffic.

New types of services with low-latency requirements have become a major challenge for the future Internet. Many optimizations, all targeting the latency reduction have been proposed. Among them, jointly re-architecting congestion control and active queue management has been particularly considered. In this effort, the L4S (Low Latency, Low Loss and Scalable Throughput) proposal aims at allowing both classic and low-latency traffic to cohabit within a single node architecture. Although this architecture sounds promising for latency improvement, it can be exploited by an attacker to perform malicious actions whose purposes are to defeat its low-latency feature and consequently make their supported applications unusable. In this paper, we analyze a set of weaknesses of L4S architecture and show that application-layer protocols such as QUIC can easily be hacked in order to exploit the over-sensitivity of those new services to network variations. By implementing undesirable flows in a real testbed and evaluating how they impact the proper delivery of low-latency flows, we demonstrate their reality and relevance for future deployments.

A composite system consists of several components which can be developed separately and deployed in distributed environments.Executing test cases on such kind of systems requires more effort due to their size and their distributed environments.A critical issue is to prioritize efficient test cases to be firstly executed.We present in this paper a framework to generate test cases and to select the efficient ones to test the composite systems with taking into account time properties.Particularly, the framework generates a set of test cases based on a model of the system, which cover a given test objective.The test cases are then prioritized in an execution order to detect quickly faults, thus reducing the efforts of test execution and increasing the effectiveness of the testing process.The framework is complemented with an open-source toolchain for automating test case generation.It has been experimentally evaluated on the European Train Control System case study.The initial results show that the approach can save 40% of test execution effort.

Software systems are more and more complex. They are usually constructed by combining several components which can be implemented separately and deployed in distributed environments. This paper presents a framework for testing these kind of systems. Particularly, each component of a system is tested by a tester and there is no communication between testers. The tester is guided by local test cases that are generated from the composition of models of components where the communication among components may be synchronous or asynchronous. The framework is complemented with a tool chain for automating test generation. The paper presents also a case study on the European Train Control System.

Service-oriented engineering is an emerging software development paradigm for distributed collaborative applications. Services are developed independently and are composed to achieve common requirements. Service choreographies define such requirements from a global perspective, based on interactions among a set of participants that are implemented as services. In this paper, we support a reliable data-aware service choreography development process through a dedicated projection. It extracts, from a choreography, a behavioral skeleton for each of its participants. The projection is valuable in a top-down approach, where developers have only to complete the skeletons with some business code in order to get a distributed application that matches the choreography requirements. The projection is also valuable in a bottom-up approach, where the skeletons can act as controllers between reused services in order to enforce the respect of the choreography. Our approach is supported with a tool that can be downloaded or used online.