  Only the chairs can edit 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.
