O-RAN introduces intelligent and flexible network control in all parts of the network. The use of controllers with open interfaces allow us to gather real time network measurements and make intelligent/informed decision. The work in this paper focuses on developing a use-case for open and reconfigurable networks to investigate the possibility to predict handover events and understand the value of such predictions for all stakeholders that rely on the communication network to conduct their business. We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events. The models were trained on real network data collected from a commercial O-RAN setup deployed in our OpenIreland testbed. Our results show that the proposed approach can be optimized for either recall or precision, depending on the defined application level objective. We also link the performance of the Machine Learning (ML) algorithm to the network operation cost. Our results show that ML-based matching between the required and available resources can reduce operational cost by more than 80%, compared to long term resource purchases.

The widespread adoption of Machine Learning (ML) is leading to an increase in processing demands. Dealing with the growing volume of data poses a significant challenge in providing accurate classification services using ML models. Offloading ML tasks to network switches presents an opportunity to tackle this challenge, offering high throughput and low latency. Nonetheless, network devices encounter limitations in resources, and programmable languages like P4 lack support for basic operations, necessary for the ML methods, including floating-point arithmetic and native repetition structures. In this paper, we investigate the use of drift detection ML models to enhance the accuracy of in-network traffic classification. The novelty lies in designing drift detection based on bitwise operations, which are well-suited for implementation within the data plane. As a proof-of-concept, we implement drift detection using the P4 language on BMv2 switches, validated with a dataset of over 2 million samples. Our results demonstrate a significant increase in classification accuracy with drift detection, while maintaining line-speed operation and quickly adapting to changes in traffic patterns.

In the fight against cyber attacks, Network Softwarization (NS) is a flexible and adaptable shield, using advanced software to spot malicious activity in regular network traffic. However, the availability of comprehensive datasets for mobile networks, which are fundamental for the development of Machine Learning (ML) solutions for attack detection near their source, is still limited. Cross-Domain Artificial Intelligence (AI) can be the key to address this, although its application in Open Radio Access Network (O-RAN) is still at its infancy. To address these challenges, we deployed an end-to-end O-RAN network, that was used to collect data from the RAN and the transport network. These datasets allow us to combine the knowledge from an in-network ML traffic classifier for attack detection to bolster the training of an ML-based traffic classifier specifically tailored for the RAN. Our results demonstrate the potential of the proposed approach, achieving an accuracy rate of 93%. This approach not only bridges critical gaps in mobile network security but also showcases the potential of cross-domain AI in enhancing the efficacy of network security measures.

In the rapidly advancing field of Underwater Acoustic Communication (UAC), channel prediction remains a major challenge, exacerbated by the complicated nature of ocean environments. This paper introduces an innovative Multi- Task Learning (MTL) framework for time-varying Underwater Acoustic (UWA) channel prediction. By decomposing the highdimensional Channel Impulse Response (CIR) prediction into interconnected tasks, the proposed framework leverages a Shared Feature Learning (SFL) layer, capturing intricate dependencies underlying UWA channels. To validate its efficacy, we conducted thorough evaluations, leveraging real-world data from two distinct at-sea experiments conducted in Wuyuan Bay, China. A comprehensive comparative study of various configurations for the SFL layer, ranging from commonly used Recurrent Neural Network (RNN)-based models to the more advanced transformer structure, further underscores the flexibility and broad applicability of our MTL framework for handling various challenging UWA environments.

We design and demonstrate a method for early detection of Denial-of-Service attacks. The proposed approach takes advantage of the OpenRAN framework to collect measurements from the air interface (for attack detection) and to dynamically control the operation of the Radio Access Network (RAN). For that purpose, we developed our near-Real Time (RT) RAN Intelligent Controller (RIC) interface. We apply and analyze a wide range of Machine Learning algorithms to data traffic analysis that satisfy the accuracy and latency requirements set by the near-RT RIC. Our results show that the proposed framework is able to correctly classify genuine vs. malicious traffic with high accuracy (i.e., 95%) in a realistic testbed environment, allowing us to detect attacks already at the Distributed Unit (DU), before malicious traffic even enters the Centralized Unit (CU).

Innovative traffic engineering functions and services require disrupting routing and forwarding mechanisms to be performed with low overhead over complex network topologies. Source routing (SR) is a prominent alternative to table-based routing for providing the needed expressiveness and agility by reducing the number of network states. This work proposes the M-PolKA, a topology-agnostic multipath source routing scheme and orchestration architecture for reliable communications, which explores special properties from the Residue Number System (RNS) polynomial arithmetic. A P4-based proof-of-concept is experimentally demonstrated using emulated and hardware prototypes. Also, use cases for revealing M-PolKA’s functionalities are tested in different scenarios in order to address problems, such as communication reliability improvement, agile path migration and fast failure reaction. Finally, low overhead for extra functionalities is observed when RNS-based SR is compared to traditional routing approaches.

Self-driving networks guided by machine-learning (ML) algorithms are the driving force for building networks of the future. ML is effective at making inferences about data that is too complex or too unpredictable for humans. The network softwarization enabled by a deep programmability approach opens up new opportunities to deploy ML at the programmable data plane. In this paper, we introduce the MAP4 as a framework that explores the feasibility of mapping ML models in programmable network devices. To achieve this, we rely on the P4 language to deploy a pre-trained model into a programmable switch, utilizing the ML model to accurately classify flows at line rate. Our approach demonstrates that ML models working as classifiers can better fit the data by using the new levels of network programmability from the P4 language. The results showed that with few packets, most of the flows are properly classified. In some use cases, with two packets in the flow, 97% of traffic can be correctly classified, and all classes are properly labeled with a maximum of four packets.

Deploying accurate machine learning algorithms into a high-throughput networking environment is a challenging task. On the one hand, machine learning has proved itself useful for traffic classification in many contexts (e.g., intrusion detection, application classification, and early heavy hitter identification). On the other hand, most of the work in the area is related to post-processing (i.e., training and testing are performed offline on previously collected samples) or to scenarios where the traffic has to leave the data plane to be classified (i.e., high latency). In this work, we tackle the problem of creating simple and reasonably accurate machine learning models that can be deployed into the data plane in a way that performance degradation is acceptable. To that purpose, we introduce a framework and discuss issues related to the translation of simple models, for handling individual packets or flows, into the P4 language. We validate our framework with an intrusion detection use case and by deploying a single decision tree into a Netronome SmartNIC (Agilio CX 2x10GbE). Our results show that high-accuracy is achievable (above 95%) with minor performance degradation, even for a large number of flows.