Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …

Intrusion Detection Systems (IDS) tackle the challenging task of detecting network attacks as
fast as possible. As this is getting more complex in modern enterprise networks, Artificial …

Network operators require real-time traffic monitoring insights to provide high performance
and security to their customers. It has been shown that artificial intelligence and machine …

Programmable data planes offer precise control over the low-level processing steps applied
to network packets, serving as a valuable tool for analysing malicious flows in the field of …

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 …

Intelligent data-planes (IDPs) can enhance network service performance and adaptation
speed by executing one or more machine learning (ML) models directly on the served flows …

The massive number of edge-connected IoT devices currently in SD-AIoT can be
weaponized to launch Distributed Denial of Service attacks. Nevertheless, centralized DDoS …

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 expanding use of Internet-of-Things (IoT) has driven machine learning (ML)-based
traffic analysis. 5G networks' standards, requiring low-latency communications for time …

A variety of model representation methods have been used in recent works to translate
machine learning models into programmable switch rules to address network classification …