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.

翻译：在网络攻防中，网络软硬化（NS）作为一种灵活且自适应的防护手段，通过先进软件在常规网络流量中识别恶意行为。然而，移动网络中全面数据集的可用性仍然有限，而这些数据集是开发攻击源头附近检测攻击的机器学习（ML）解决方案的基础。跨域人工智能（AI）可能是解决这一问题的关键，尽管其在开放式无线接入网络（O-RAN）中的应用仍处于初期阶段。为应对这些挑战，我们部署了一个端到端的O-RAN网络，用于从RAN和传输网络收集数据。这些数据集使我们能够结合网络内ML流量分类器的攻击检测知识，以增强专为RAN定制的基于ML的流量分类器的训练。我们的结果展示了所提方法的潜力，实现了93%的准确率。该方法不仅弥合了移动网络安全中的关键缺口，还展示了跨域AI在提升网络安全措施有效性方面的潜力。