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Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems.

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Fifth generation (5G) technologies will boost the capacity and ease the management of mobile networks. Emerging virtualization and softwarization technologies enable more flexible customization of network services and facilitate cooperation between different actors. However, solutions are needed to enable users, operators, and service providers to gain an up-to-date awareness of the security and trustworthiness of 5G systems. We describe a novel framework and enablers for security monitoring, inferencing, and trust measuring. The framework leverages software-defined networking and big data technologies to customize monitoring for different applications. We present an approach for sharing security measurements across administrative domains. We describe scenarios where the correlation of multi-domain information improves the accuracy of security measures with respect to two threats: end-user location tracking and Internet of things (IoT) authentication storms. We explore the security characteristics of data flows in software networks dedicated to different applications with a mobile network testbed.