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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Today large amounts of security and privacy-critical data are transferred, processed and stored in external cloud services. However, with many offerings, you need to be either an ignoramus or a daredevil to surrender such data. For the cautious, trustworthy, sufficient and credible evidence of the actual security, privacy and trust level is a prerequisite for considering cloud services for critical applications. We present results from an expert interview study investigating the needs of security, privacy and trust measurement in the cloud. Furthermore, research directions for metrics development are discussed based on the interviews.

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