# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### Deep Learning-Based Intrusion Detection Framework  
The cellular network can be a target of different attacks that lead to failure of the network services. The concept of self-protection is important for SONs in 5G. Traditionally, security requirements were for the protection of voice and text data. User identification was solved by implementing a secure SIM. The networks implemented mutual authentication between the network and users. The communication path between communicators was also made secure.  
While 5G brings speed, more flexibility, new services, and easy communication, it also brings new kinds of threats. A usual threat in computer networks is the denial of service (DoS) attack. The DoS is a type of attack where a huge amount of traffic is generated on particular nodes, so much that the nodes are not able to respond to requests anymore. As 5G is going to build on the Internet of Things (IoT) concept, it will connect many devices and some will be very small. Often, the security of such small and simple devices cannot be implemented at the node level. This means that such nodes are highly vulnerable, especially to DoS attacks. Other important factors to consider are that 5G will connect many different types of networks. This means that there will be different levels of protection needed,for example, a hospital connected via the network would require special treatment as compared to a tire shop. All in all, many aspects of 5G are still under development; however, some high level decisions about security and privacy need to be agreed on between stakeholders. In the literature, many methods have been proposed for intrusion detection in cellular networks [75; 76].  
Machine learning methods have been successful in implementing pattern-based intrusion detection for cellular networks. Therefore, there is the potential to use machine learning and also deep learning [77] techniques in 5G. In 5G, deep learning could help in the detection and mitigation of DoS attacks via traffic monitoring and proactive prediction of unforeseen events. Other solutions could be end user equipment protection from malware, access network intrusion detection, and mobile core and IP network protection.  
User equipment, such as smart-phones and tablets, is vulnerable due to the variety of connectivity options (LTE, 5G, WiFi, etc.), third-party apps, and operating systems, and the over increased data transmission. Mobile malware can get installed on user devices via apps downloaded from untrusted app stores. Malware can exploit and steal personal user data, enable DoS attacks, spam the network from a user's device, or launch a distributed DoS attack. Attacks on the access network can be for the purpose of stealing bandwidth, tracking a specific user in a cell or multiple cells, and physical tempering of the hardware. The mobile core and IP network are also vulnerable to DoS attacks. A major drawback for ML or deep learning approaches in the detection of vulnerabilities in these networks could be the unavailability of labeled data. However, possible solutions could be semi-supervised learning approaches, where any small set of labeled data can be useful.