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Only the chairs can edit In the fight against cyber attacks, NetworkSoftwarization (NS) is a flexible and adaptable shield, using advanced softwareto spot malicious activity in regular network traffic. However, theavailability of comprehensive datasets for mobile networks, which arefundamental for the development of Machine Learning (ML) solutions for attackdetection near their source, is still limited. Cross-Domain ArtificialIntelligence (AI) can be the key to address this, although its application inOpen Radio Access Network (O-RAN) is still at its infancy. To address thesechallenges, we deployed an end-to-end O-RAN network, that was used to collectdata from the RAN and the transport network. These datasets allow us to combinethe knowledge from an in-network ML traffic classifier for attack detection tobolster the training of an ML-based traffic classifier specifically tailoredfor the RAN. Our results demonstrate the potential of the proposed approach,achieving an accuracy rate of 93%. This approach not only bridges critical gapsin mobile network security but also showcases the potential of cross-domain AIin enhancing the efficacy of network security measures.

Cross-Domain AI for Early Attack Detection and Defense Against Malicious Flows in O-RAN