Recent management paradigms for software-defined infrastructures bring more agility to the creation and operation of digital services, but also introduce new cyber-security issues due to fast-changing environments, dynamic topologies, and wider attack surfaces. Rigid and statically-configured architectures are no longer suitable for the detection of cyber-attacks in mixed cloud/6G/IoT environments, hence new frameworks must be designed that are more flexible and adaptable to become cognitive. A fundamental step in this direction is represented by the adoption of common interfaces to orchestrate heterogeneous and multi-vendor security functions in a homogeneous way. In this article, we consider two recent interfaces to security functions that are representative of different approaches and industrial domains, namely I2NSF and OpenC2. We briefly review the latest advances in their definition, provide a deep comparison, and outline major limitations and research challenges for concrete application scenarios. The main purpose of our work is to make an unbiased evaluation of the current status of these standards and to encourage researchers to actively contribute to the development of the standards by adopting them and proposing further extensions and refinements.

Interface To Security Functions: An Overview And Comparison Of I2nsf And Openc2

Thanks to its cloud-native design, the 5G architecture can be easily deployed and operated with modern cloud paradigms. The availability of open-source implementations of both the 5G Core Network (5GC) and Radio Access Network (RAN) are of paramount importance to investigate management, performance, and security issues that arise from this new operational model. However, such software usually does not include the necessary deployment scripts to easily and quickly run it in Kubernetes clusters.In this paper, we describe our set of service templates for emulating network attacks in (and against) 5G systems deployed in the cloud. Our contribution consists of the necessary automation workflows for building manifest files, setting up and running research testbeds in a matter of minutes. The templates include a scenario with broad applicability, namely attacks to Internet services from botnets in the RAN, also taking into account the presence of traffic from licit users. Real-time scalability of the botnet and licit users allows to easily generate different workload conditions; additionally, different attacks can be reproduced by simply replacing the container images for the Internet service, licit clients, and botnet nodes. Evaluation considers the time to deploy the cloud-native application and verification of its correct operation.

Service Templates to Emulate Network Attacks in Cloud-Native 5G Infrastructures

Adaptive monitoring, detection, and response for agile digital service chains

The combination of virtualization techniques with capillary computing and storage resources allows the instantiation of Virtual Network Functions throughout the network infrastructure, which brings more agility in the development and operation of network services. Beside forwarding and routing, this can be also used for additional functions, e.g., for security purposes. In this paper, we present a framework to systematically create security analytics for virtualized network services, specifically targeting the detection of cyber-attacks. Our framework largely automates the deployment of security sidecars into existing service templates and their interconnection to an external analytics platform. Notably, it leverages code augmentation techniques to dynamically inject and remove inspection probes without affecting service operation. We describe the implementation of a use case for the detection of DNS amplification attacks in virtualized 5G networks, and provide extensive evaluation of our innovative inspection and detection mechanisms. Our results demonstrate better efficiency with respect to existing network monitoring tools in terms of CPU usage, as well as good accuracy in detecting attacks even with variable traffic patterns.

Automating Mitigation of Amplification Attacks in NFV Services

This paper presents the details of a novel approach, based on edge and advanced privacy preserving solutions, that tries to accelerate the adoption of personal data federation for the benefit of the evolution of valuable advanced AI models. The approach focuses on the establishment of high degree of trust between data owner and data management infrastructure so that consent in data processing is given by means of functional and enforceable options applicable at all levels of workloads and processes. The overall set of solutions will be delivered as an open-source set of implementations in the context of the PAROMA-MED project.

Federated machine learning through edge ready architectures with privacy preservation as a service

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Automating Mitigation of Amplification Attacks in NFV Services

Federated machine learning through edge ready architectures with privacy preservation as a service