## 6 Future Directions
### Security and Secrecy  
Due to the coverage areas of 6G and satellite networks, the number of places and users they can reach, and the scale of their services covering everywhere, security and secrecy requirements are also increasing. However, ensuring secrecy and security measures is becoming more difficult. Thanks to the full effective deployment of 6G and space networks, their boundaries are likely to be unpredictably wide. It is envisaged that networks at this scale will be designed with intelligent systems, such as AI, and decentralized systems, such as blockchain [56]. These systems are relatively new systems. It is necessary to expand the scope of the studies conducted to ensure security and confidentiality in these systems. In addition, it has not been clearly defined how measures should be taken if the potential possessed by AI systems is used as a counterattack. Data on how to prevent security and secrecy violations at the point of using the stored data in AI algorithms are also very limited. AI acts as both a poison and an antidote. These technologies, which will inevitably take their place in life, need innovative solutions at the point of data privacy. Quantum encryption methods can be used to protect security and secrecy in next-generation networks [68]. In 2018, [PERSON] et al. worked on the integration of deep learning networks into networks for traffic control and routing management. In this study, the authors aimed to produce actions by evaluating historical data with deep learning methods. Traditional network routing protocols are unable to analyze historical data and generate actions based on it. The application of deep learning techniques to process this data and manage network traffic has become a trending area of research. In this research, they suggest a new routing technique that they propose to employ in future wired and wireless networks. They present a real-time deep learning-based smart network traffic control technique employing Deep Convolutional Neural Networks (deep CNNs) [143]. In 2020, [PERSON] et al. conducted research on machine learning techniques to be used in 6G networks. In this study, the difficulties that may be encountered in the integration of machine learning algorithms with 6G networks are examined. They presented the study from a broad perspective. They examined the challenges of machine learning in 6G applications under 10 headings from the perspectives of communication, learning, and computing [144]. In 2020, [PERSON] et al. proposed a load balance algorithm called \"reliable intelligent routing mechanism (RIRM)\" to manage congestion in 5 GC (5G Core) networks. This algorithm aims to select the shortest path to adjust the load balance on the network. To implement this algorithm, they added an RIRM traffic tracker that monitors all current routing information on the data plane and also added an RIRM traffic controller to the control plane of the 5 GC network. The RIRM traffic tracker transmits the information it collects to the RIRM traffic controller. As a result of this flow, they try to predict the best route. In the experimental results, it turned out to be 102 and 22 times better compared to well-known round-robin load balance algorithms by way of packet loss rate latency, respectively. Moreover, the mean data throughput was boosted by 13% [145]. In 2021, [PERSON] et al. carried out a detailed study on 6G satellite-aerial networks. They provided a detailed review of the various layers in satellite networks and high-altitude platform (HAP) networks and two different solution proposals, such as radio-frequency (RF) and free-space optics (FSOs). At the point of future communication technology, satellite networks are at a point that cannot be ignored, along with the advantage of coverage. However, according to terrestrial networks, the volume of challenges in these networks is growing [122]. In 2022, [PERSON] et al.