# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 10 AI Support for 5G Operations and Services
### Service Management  
More intelligence built in to the 5G system will allow for a shift from managing networks to managing services. Intelligent functions can be customized for each of these services allowing them to operate more resiliently and securely, taking the mobile network to a new level of innovation for the benefit of industry and society [114]. While digital transformation accelerates service innovation, it also requires automation abilities, as mobile operators need a much more flexible network to satisfy the increasing demands of the new services [115].  
Already in 2014, Ref. [116] showed the effectiveness of proactive caching, a service-level management mechanism proposed for 5G systems, in two case studies that exploited the spatial and social structure of the network. First, in order to alleviate backhaul congestion, they proposed a mechanism whereby files are proactively cached during off-peak periods based on file popularity and correlations among user and file patterns. Second, leveraging social networks and D2D communications, they proposed a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications.  
The authors of [117] presented the limitations of the current network and service management and described in detail the challenges that 5G is expected to face from a management perspective. They presented a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. They argued that machine learning could provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.  
In [118] the authors proposed an intelligent IDS taking the advances in software-defined technology and artificial intelligence based on software-defined 5G architecture. The solution flexibly integrates security function modules which are adaptively invoked under centralised management and control with a global view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results proved that the intelligent IDS achieved good performance with low overhead, and the selected machine learning algorithms showed good accuracy in flow-based classification.  
The authors of [119] proposed a novel 5G-oriented cyberdefense architecture to identify cyberthreats in 5G mobile networks efficient and quickly enough. For this, their architecture used deep learning techniques to analyze network traffic by extracting features from network flows. Moreover, their proposal allowed adapting, automatically, the configuration of the cyberdefense architecture in order to manage traffic fluctuation, aiming both to optimize the computing resources needed in each particular moment and to fine tune the behavior and the performance of analysis and detection processes.  
The application possibilities of deep learning in mobile telecommunications network and service management has first been summarized by [120], as shown in Table 10. The FCAPS represent Fault Configuration, Accounting, Performance and Security Management respectively--and these are matched with deep learning target applications, such as prediction, anomaly detection, clustering, or classification. Besides, the authors demonstrated a use-case where they manage to predict call-establishment errors to happen within the IP Multimedia Subsystem (IMS) based on previously learned correlations with delays on certain network paths.