The cybersecurity landscape is fraught with challenges stemming from the increasing volume and complexity of security alerts. Traditional manual or semi-automated approaches to threat analysis and incident response often result in significant delays in identifying and mitigating security threats. In this paper, we address these challenges by proposing AI4SOAR, a security intelligence tool for automated incident response. AI4SOAR leverages similarity learning techniques and integrates seamlessly with the open-source SOAR platform Shuffle. We conduct a comprehensive survey of existing open-source SOAR platforms, highlighting their strengths and weaknesses. Additionally, we present a similarity-based learning approach to quickly identify suitable playbooks for incoming alerts. We implement AI4SOAR and demonstrate its application through a use case for automated incident response against SSH brute-force attacks.

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.

Despite its enormous economical and societal impact, lack of human-perceived control and safety is re-defining the design and development of emerging AI-based technologies. New regulatory requirements mandate increased human control and oversight of AI, transforming the development practices and responsibilities of individuals interacting with AI. In this paper, we present the SPATIAL architecture, a system that augments modern applications with capabilities to gauge and monitor trustworthy properties of AI inference capabilities. To design SPATIAL, we first explore the evolution of modern system architectures and how AI components and pipelines are integrated. With this information, we then develop a proof-of- concept architecture that analyzes AI models in a human-in-the- loop manner. SPATIAL provides an AI dashboard for allowing individuals interacting with applications to obtain quantifiable insights about the AI decision process. This information is then used by human operators to comprehend possible issues that influence the performance of AI models and adjust or counter them. Through rigorous benchmarks and experiments in real- world industrial applications, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness, however, this in turn increases the complexity of developing and maintaining systems implementing AI. Our work highlights lessons learned and experiences from augmenting modern applications with mechanisms that support regulatory compliance of AI. In addition, we also present a road map of on-going challenges that require attention to achieve robust trustworthy analysis of AI and greater engagement of human oversight.

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 prevalence of encrypted Internet traffic has resulted in a pressing need for advanced analysis techniques for traffic analysis and classification. Traditional rule-based and signature-based approaches have been hindered by the introduction of network encryption methods. With the emergence of machine learning (ML) and deep learning (DL), several preliminary works have been developed for anomaly detection in encrypted network traffic. However, complex Artificial Intelligence (AI) models like neural networks lack explainability, limiting the understanding of their predictions. To address this limitation, eXplainable Artificial Intelligence (XAI) has emerged, aiming to provide users with a rationale for understanding AI system outputs and fostering trust. However, existing explainable frameworks still lack comprehensive support for adversarial attacks and defenses.

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.

This work presents a Moving Target Defense (MTD) framework for the protection of network slices and virtual resources in a telco cloud environment. The preliminary implementation provides a closed-loop security management of services with proactive MTD operations to reduce the success probability of attacks, and reactive MTD operations, empowered by a tampering detection and a traffic-based anomaly detection system. MTD strategies are adaptive and optimized with deep reinforcement learning (deep-RL) for balancing costs, security, and availability goals defined in a Multi-Objective Markov Decision Process (MOMDP).

The adoption of 5G services depends on the capacity to provide high-value services. In addition to enhanced performance, the capacity to deliver Security Service Level Agreements (SSLAs) and demonstrate their fulfillment would be a great incentive for the adoption of 5G services for critical 5G Verticals (e.g., service suppliers like Energy or Intelligent Transportation Systems) subject to specific industrial safety, security or service level rules and regulations (e.g., NIS or SEVESO Directives). Yet, responsibilities may be difficult to track and demonstrate because 5G infrastructures are interconnected and complex, which is a challenge anticipated to be exacerbated in future 6G networks. This paper describes a demonstrator and a use case that shows how 5G Service Providers can deliver SSLAs to their customers (Service Owners) by leveraging a set of network enablers developed in the INSPIRE-5Gplus project to manage their accountability, liability and trust placed in subcomponents of a service (subcontractors). The elaborated enablers are in particular a novel sTakeholder Responsibility, AccountabIity and Liability deScriptor (TRAILS), a Liability-Aware Service Management Referencing Service (LASM-RS), an anomaly detection tool (IoT-MMT), a Root Cause Analysis tool (IoT-RCA), two Remote Attestation mechanisms (Systemic and Deep Attestation), and two Security-by-Orchestration enablers (one for the 5G Core and one for the MEC).

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.

International audience

The promise of disparate features envisioned by the 3GPP for 5G, such as offering enhanced Mobile Broadband connectivity while providing massive Machine Type Communications likely with very low data rates and maintaining Ultra Reliable Low Latency Communications requirements, create a very challenging environment for protecting the 5G networks themselves and associated assets. To overcome such complexity, future 5G networks must employ a very high degree of network and service management automation, which is a security challenge by itself as well as an opportunity for smarter and more efficient security functions. In this paper, we present the smart, trustworthy and liable 5G security platform being designed and developed in the INSPIRE-5Gplus1 project. This platform takes advantage of new techniques such as Machine Learning (ML), Artificial Intelligence (AI), Distributed Ledger Technologies (DLT), network softwarization and Trusted Execution Environment (TEE) for closed-loop and end-to-end security management following a zero-touch model in 5G and Beyond 5G networks. To this end, we specifically elaborate on two key aspects of our platform, namely security management with Security Service Level Agreements (SSLAs) and liability management, in addition to the description of the overall architecture.

Anomaly detection remains a challenging task due to both the ever more complex functions that need to be executed and the evolution of current networking devices which induces limitation of computational resources such as the Internet of Things (IoT). Furthermore, results of anomaly function computations can be repeated gradually over time or executed in neighboring nodes, thus leading to a waste of such limited computing resources in constrained nodes. To tackle these issues, the content-centric paradigm enhanced with computing features offers a promising solution to reduce the computation resources and finally improve the scalability of anomaly detection functions. In this paper, we propose a first step toward a content-oriented control plane which enables the distribution of the processing and the sharing of results of anomaly detection functions in the network. We present the way we leverage NFN to support Bayesian Network inference to detect anomalies in network traffic. The relevance and performance of our proposed approach are demonstrated by considering the Content Poisoning Attack (CPA) through numerous experiment data.

Named data networking (NDN) is a disruptive yet promising architecture for the future Internet, in which the content diffusion mechanisms are shifted from the conventional host-centric to content-centric ones so that the data delivery can be significantly improved. After a decade of research and development, NDN and the related NDN forwarding daemon implementations are now mature enough to enable stakeholders, such as telcos, to consider them for a real deployment. Consequently, NDN and IP will likely cohabit, and the future Internet may be formed of isolated administrative domains, each deploying one of these two network paradigms. The security question of the resulting architecture naturally arises. In this paper, we consider the case of denial of service. Even though the interest flooding attack (IFA) has been largely studied and mitigated through NACK packets in pure NDN networks, we demonstrate in this paper through experimental assessments that there are still some ways to mount such an attack, and especially in the context of coupling NDN with IP, which can hardly be addressed by current solutions. Subsequently, we leverage the hypothesis testing theory to develop a generalized likelihood ratio test adapted to evolve IFA attacks. Simulations show the relevance of the proposed model for guaranteeing the prescribed probability of false alarm and highlight the trade-off between detection power and delay. Finally, we consider a real deployment scenario where NDN is coupled with IP to carry HTTP traffic. We show that the model of IFA attacks is not very accurate in practice and further develops a sequential detector to keep a high detection accuracy. By considering data from the testbed, we show the efficiency of the overall detection method.

5G is envisioned as a transformation of the communications architecture towards multi-tenant, scalable and flexible infrastructure, which heavily relies on virtualised network functions and programmable networks. In particular, orchestration will advance one step further in blending both compute and data resources, usually dedicated to virtualisation technologies, and network resources into so-called slices. Although 5G security is being developed in current working groups, slice security is seldom addressed.

Named Data Networking (NDN) is the most mature proposal of the Information Centric Networking paradigm, a clean-slate approach for the Future Internet. Although NDN was designed to tackle security issues inherent to IP networks natively, newly introduced security attacks in its transitional phase threaten NDN's practical deployment. Therefore, a security monitoring plane for NDN is indispensable before any potential deployment of this novel architecture in an operating context by any provider. We propose an approach for the monitoring and anomaly detection in NDN nodes leveraging Bayesian Network techniques. A list of monitored metrics is introduced as a quantitative measure to feature the behavior of an NDN node. By leveraging the hypothesis testing theory, a micro detector is developed to detect whenever the metric significantly changes from its normal behavior. A Bayesian network structure that correlates alarms from micro detectors is designed based on the expert knowledge of the NDN specification and the NFD implementation. The relevance and performance of our security monitoring approach are demonstrated by considering the Content Poisoning Attack (CPA), one of the most critical attacks in NDN, through numerous experiment data collected from a real NDN deployment.

Software Defined Network (SDN) is an advanced approach to designing dynamic, manageable, cost-effective, and adaptable network architectures. SDN will play a key role as an enabler for 5G and future networks. Transferring network monitoring functions to a software entity working in conjunction with configurable hardware accelerators through a scheme called Software Defined Monitoring (SDM) is one promising way to attain the dynamism necessary for the monitoring of the next generation-networks. In this paper, we propose a novel SDM architecture for future mobile backhual networks. As an SDN solution, the proposed architecture provides more granular and dynamic network management functions through its programmable interface, centralized control, and virtualized abstractions. At the same time, the SDM framework intuitively seem prone to various challenges that come with the separation of the control and data planes of middleboxes. This paper collects specific opportunities, vulnerabilities as well as challenges related to SDM. It also highlights how SDM can be used to solve the current limitations in legacy monitoring systems. The feasibility of the proposed SDM architecture is verified by using a testbed implementation.

Network operators are currently very cautious before deploying a new network equipment. This is done only if the new networking solution is fully monitored, secured and can provide rapid revenues (short Return of Investment). For example, the NDN (Named Data Networking) solution is admitted as promising but still uncertain, thus making network operators reluctant to deploy it. Having a flexible environment would allow network operators to initiate the deployment of new network solutions at low cost and low risk. The virtualization techniques, appeared a few years ago, can help to provide such a flexible networking architecture. However, with it, emerge monitoring and security issues which should be solved. In this paper, we present our secure virtualized networking environment to deploy new functions and protocol stacks in the network, with a specific focus on the NDN use-case as one of the potential Future Internet technology. As strong requirements for a network operator, we then focus on monitoring and security components, highlighting where and how they can be deployed and used. Finally, we introduce our preliminary evaluation, with a focus on security, before presenting the test bed, involving end-users consuming real contents, that we will set up for the assessment of our approach.

MMT (Mont image Monitoring Tool) is a monitoring solution that combines: data capture, filtering and storage, events extraction, statistics collection, traffic analysis and reporting. In the context of the PIMI and DIAMONDS projects, Mont image is developing MMT-Security: a security analysis solution (part of MMT) that inspects network traffic against a set of security properties denoting both security rules and attacks. This tool has been applied to an industrial case study provided by Thales Group that consists of a QoS-aware ad-hoc radio communication protocol.

The service-oriented approach is becoming more and more popular to integrate highly heterogeneous systems. Web services are the natural evolution of conventional middleware technologies to support Web-based and enterprise level integration. Formal testing of such Web-based technology is a key point to guarantee its reliability. In this paper, we choose a non-intrusive approach based on monitoring to propose a conformance passive testing methodology to check that a composed Web service respects its functional requirements. This methodology is based on a set of formal invariants representing properties to be tested including data and time constraints. Passive testing of an industrial system (that uses a composition of Web services) is briefly presented to demonstrate the effectiveness of the proposed approach.

Although the adoption of the IP Multimedia Subsystem (IMS) keeps growing, IMS applications are often integrated to the system without being formally tested. In this work, we are interested in the IMS Push over Cellular (PoC) service, an OMA standard. We propose a conformance passive testing approach to check that its implementation respects the main standard requirements. This approach is based on a set of formal invariants representing the most relevant expected properties to be tested. Two testing phases are applied: the verification of the invariants against the service specification and their testing on the PoC collected execution traces.

The complexity and the variety of the deployed time dependent systems, as well as the high degree of reliability required for their global functioning, justify the care provided to the design of the best possible tests. Moreover,it is significant to automate these steps with an aim of reducing the time and the development cost and especially of increasing the reliability of the offered products. In this paper, we present two different tools to test systems with time constraints. The first one allows to automatically generate test cases based on model-based active testing techniques. Whereas the second tool is based on passive testing approach to check that the collected system traces respect a set of formal properties called Invariants.