arXiv subjects

Filippo Rebecchi

Publications and source records attributed to Filippo Rebecchi.

6 recordsLinked to original sources

arXiv2025

Beyond Static Thresholds: Adaptive RRC Signaling Storm Detection with Extreme Value Theory

In 5G and beyond networks, the radio communication between a User Equipment (UE) and a base station (gNodeB or gNB), also known as the air interface, is a critical component of network access and connectivity. During the connection establishment procedure, the Radio Resource Control (RRC) layer can be vulnerable to signaling storms, which threaten the availability of the radio access control plane. These attacks may occur when one or more UEs send a large number of connection requests to the gNB, preventing new UEs from establishing connections. In this paper, we investigate the detection of such threats and propose an adaptive threshold-based detection system based on Extreme Value Theory (EVT). The proposed solution is evaluated numerically by applying simulated attack scenarios based on a realistic threat model on top of real-world RRC traffic data from an operator network. We show that, by leveraging features from the RRC layer only, the detection system can not only identify the attacks but also differentiate them from legitimate high-traffic situations. The adaptive threshold calculated using EVT ensures that the system can work under diverse traffic conditions. The results show high accuracy, precision, and recall values (above 93%), and a low detection latency even under complex conditions.

cs.CR↗

arXiv2025

Managing Differentiated Secure Connectivity using Intents

Mobile networks in the 5G and 6G era require to rethink how to manage security due to the introduction of new services, use cases, each with its own security requirements, while simultaneously expanding the threat landscape. Although automation has emerged as a key enabler to address complexity in networks, existing approaches lack the expressiveness to define and enforce complex, goal-driven, and measurable security requirements. In this paper, we propose the concept of differentiated security levels and leveraging intents as a management framework. We discuss the requirements and enablers to extend the currently defined intent-based management frameworks to pave the path for intent-based security management in mobile networks. Our approach formalizes both functional and non-functional security requirements and demonstrates how these can be expressed and modeled using an extended TM Forum (TMF) intent security ontology. We further discuss the required standardization steps to achieve intent-based security management. Our work aims at advance security automation, improve adaptability, and strengthen the resilience and security posture of the next-generation mobile networks.

cs.CR↗

arXiv2025

Measuring Security in 5G and Future Networks

In today's increasingly interconnected and fast-paced digital ecosystem, mobile networks, such as 5G and future generations such as 6G, play a pivotal role and must be considered as critical infrastructures. Ensuring their security is paramount to safeguard both individual users and the industries that depend on these networks. An essential condition for maintaining and improving the security posture of a system is the ability to effectively measure and monitor its security state. In this work we address the need for an objective measurement of the security state of 5G and future networks. We introduce a state machine model designed to capture the security life cycle of network functions and the transitions between different states within the life cycle. Such a model can be computed locally at each node, or hierarchically, by aggregating measurements into security domains or the whole network. We identify three essential security metrics -- attack surface exposure, impact of system vulnerabilities, and effectiveness of applied security controls -- that collectively form the basis for calculating the overall security score. With this approach, it is possible to provide a holistic understanding of the security posture, laying the foundation for effective security management in the expected dynamic threat landscape of 6G networks. Through practical examples, we illustrate the real-world application of our proposed methodology, offering valuable insights for developing risk management and informed decision-making strategies in 5G and 6G security operations and laying the foundation for effective security management in the expected dynamic threat landscape of 6G networks.

cs.CR↗

arXiv2025

RRC Signaling Storm Detection in O-RAN

The Open Radio Access Network (O-RAN) marks a significant shift in the mobile network industry. By transforming a traditionally vertically integrated architecture into an open, data-driven one, O-RAN promises to enhance operational flexibility and drive innovation. In this paper, we harness O-RAN's openness to address one critical threat to 5G availability: signaling storms caused by abuse of the Radio Resource Control (RRC) protocol. Such attacks occur when a flood of RRC messages from one or multiple User Equipments (UEs) deplete resources at a 5G base station (gNB), leading to service degradation. We provide a reference implementation of an RRC signaling storm attack, using the OpenAirInterface (OAI) platform to evaluate its impact on a gNB. We supplement the experimental results with a theoretical model to extend the findings for different load conditions. To mitigate RRC signaling storms, we develop a threshold-based detection technique that relies on RRC layer features to distinguish between malicious activity and legitimate high network load conditions. Leveraging O-RAN capabilities, our detection method is deployed as an external Application (xApp). Performance evaluation shows attacks can be detected within 90ms, providing a mitigation window of 60ms before gNB unavailability, with an overhead of 1.2% and 0% CPU and memory consumption, respectively.

cs.CR↗

arXiv2022

ENCODE: Encoding NetFlows for Network Anomaly Detection

NetFlow data is a popular network log format used by many network analysts and researchers. The advantages of using NetFlow over deep packet inspection are that it is easier to collect and process, and it is less privacy intrusive. Many works have used machine learning to detect network attacks using NetFlow data. The first step for these machine learning pipelines is to pre-process the data before it is given to the machine learning algorithm. Many approaches exist to pre-process NetFlow data; however, these simply apply existing methods to the data, not considering the specific properties of network data. We argue that for data originating from software systems, such as NetFlow or software logs, similarities in frequency and contexts of feature values are more important than similarities in the value itself. In this work, we propose an encoding algorithm that directly takes the frequency and the context of the feature values into account when the data is being processed. Different types of network behaviours can be clustered using this encoding, thus aiding the process of detecting anomalies within the network. We train several machine learning models for anomaly detection using the data that has been encoded with our encoding algorithm. We evaluate the effectiveness of our encoding on a new dataset that we created for network attacks on Kubernetes clusters and two well-known public NetFlow datasets. We empirically demonstrate that the machine learning models benefit from using our encoding for anomaly detection.

cs.LG↗

arXiv2022

Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster

These days more companies are shifting towards using cloud environments to provide their services to their client. While it is easy to set up a cloud environment, it is equally important to monitor the system's runtime behaviour and identify anomalous behaviours that occur during its operation. In recent years, the utilisation of \ac{rnn} and \ac{dnn} to detect anomalies that might occur during runtime has been a trending approach. However, it is unclear how to explain the decisions made by these networks and how these networks should be interpreted to understand the runtime behaviour that they model. On the contrary, state machine models provide an easier manner to interpret and understand the behaviour that they model. In this work, we propose an approach that learns state machine models to model the runtime behaviour of a cloud environment that runs multiple microservice applications. To the best of our knowledge, this is the first work that tries to apply state machine models to microservice architectures. The state machine model is used to detect the different types of attacks that we launch on the cloud environment. From our experiment results, our approach can detect the attacks very well, achieving a balanced accuracy of 99.2% and an F1 score of 0.982.

cs.DC↗