## 4 Security Threat Landscape in 5G Networks
### Analysis of 5G Security Challenges in Conjunction with Edge Computing  
The integration of edge computing into 5G networks enhances performance through reduced latency and increased capacity for real-time data processing. However, this shift also brings new security challenges. Firstly, the decentralization of data processing, inherent to edge computing, increases the number of attack vectors. The data are processed closer to the user, which expands the potential points of attack and increases the complexity of implementing centralized security measures [68]. Moreover, edge nodes are often less powerful than centralized \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Security Solutions** & **Description** & **Advantages** & **Challenges** & **Use Cases** \\ \hline  & Implements IPS to proactively block or prevent detected security threats. & - & Actively prevents security threats from causing damage. & - & Possibility of false positives and false negatives. & - & Proactive security measures for mMTC networks. \\ Intrusion prevention systems (IPSs) & It enhances the security posture of mMTC networks by stopping potential attacks before they can cause harm. & - & Reduces the impact of security incidents. & - & - \\ \hline  & Harnesses machine learning (ML) and artificial intelligence (AI) techniques to analyze vast amounts of network data. & - & Adaptive and self-learning capabilities. & - & Requires extensive training and data. & - & Advanced threat detection and prevention in mMTC networks. \\ Machine learning (ML) and artificial intelligence (AI) and anomalies, greatly enhancing the ability to detect and prevent cyberattacks in mMTC networks. & - & Effective in identifying complex attack patterns. & - & May generate false positives without proper tuning. & - & mMTC networks. \\ \hline  & Deploys rigorous authentication mechanisms to ensure that only authorized devices and entities can access the mMTC network. & - & Ensures only trusted devices access the network. Protects sensitive data from unauthorized access to the network. & - & May introduce latency in the authentication process. & - & \\  & Access control measures are & - & Protects sensitive data from unauthorized access to the network and sensitive data. & - & Requires robust management of authentication credentials. & - & Secure access control in mMTC applications. \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._servers in terms of computational capacity, making them potentially more vulnerable to cyberattacks. Deploying robust security measures on edge nodes can be challenging due to these resource limitations. Furthermore, edge computing's dependency on the IoT devices, which are known for their diverse security levels and standards, adds another layer of complexity to the security challenges [69]. This diversity often results in an inconsistent security landscape, which can be exploited by attackers. Privacy is also a concern with edge computing in 5G networks. Data are processed closer to the user device, making it more vulnerable to breaches if not adequately protected. The potential risks and vulnerabilities in edge computing within 5G networks are multifaceted, resulting from various factors, such as the increased number of devices, decentralization of data processing, and lack of uniform security standards. Edge computing dramatically increases the number of devices involved in data processing and communication, each representing a potential point of entry for attackers. The heterogeneous nature of these devices further complicates the security landscape, as different devices might have different vulnerabilities [70]. Data integrity is another major concern. The decentralization of data processing means that data might travel through various nodes before reaching its destination, increasing the risk of data corruption or manipulation.