## 7 Overview of Deep Learning in 5G Security
### Deep Learning Applications in 5G Security
#### 7.3.1 Improving Intrusion Detection Systems  
IDSs are a fundamental component of network security, serving as a first line of defense against potential security breaches. They function by identifying unusual or suspicious activity in network traffic that could indicate an attempted intrusion [144]. However, traditional IDSs often rely on predefined rules and signatures to detect intrusions, which  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{2}{p{113.8 pt}}{**Deep Learning Techniques in 5G Security**} & \multicolumn{2}{c}{**Applications**} & \multicolumn{2}{c}{**Strengths**} & \multicolumn{2}{c}{**Weaknesses**} \\ \hline Convolutional neural networks & - & Image-based authentication & - & Efficient in spatial data analysis & - & Requires large labeled datasets \\ (CNNs) & - & Traffic pattern recognition & - & Robust to image transformations & - & Computationally intensive \\ \hline Recurrent neural networks & - & Sequence-based anomaly detection & - & Effective for sequential data & - & Difficult to train Susceptible to vanishing and exploding gradient problems \\ (RNNs) & - & Time-series analysis in network traffic & - & Ability to generate new data samples & - & Requires balanced dataset for effective training \\ \hline Generative adversarial networks & - & Data augmentation for intrusion detection & - & Ability to generate new data samples & - & Requires balanced dataset for effective training \\ (GANs) & - & Anomaly detection & - & Effective for semi-supervised learning & - & Complexity in model convergence \\ \hline Autoencoders & - & Anomaly detection & - & Effective for dimensionality reduction & - & Risk of overfitting Sensitivity to hyperparameters \\  & - & Feature reduction for ML models & - & Capable of learning data representations & - & Requires substantial computing resources \\ \hline \multirow{3}{*}{Transformer networks} & - & Natural Language Processing for cybersecurity & - & Capable of capturing long-range dependencies & - & Requires substantial computing resources \\  & - & (e.g., phishing detection) & - & Scalable and parallelizable architecture & - & May need large datasets for effective training \\ \hline \end{tabular}
\end{table}
Table 14: Deep learning security solutions in 5G [132; 133; 134; 135; 136; 137; 138; 139; 140; 141].  
may not be effective against novel or sophisticated attacks. This is where deep learning can make a significant contribution to improving the efficacy of IDSs in 5G networks. By applying deep learning models, IDSs can be trained to identify both known and unknown threats in real time. For instance, deep learning techniques such as CNNs have been used to develop IDSs that can learn to recognize intrusion attempts based on patterns in network traffic [140]. Similarly, RNNs, which can process sequential data, can identify temporal patterns in network traffic that may indicate an ongoing or imminent attack.  
Another way deep learning can enhance IDSs is by reducing the number of false positives, which are the incorrect identification of normal activities as intrusions. High rates of false positives can overwhelm network administrators and potentially result in genuine threats being overlooked. Deep learning algorithms can learn the normal behavioral patterns of a network over time and become more accurate in distinguishing between actual intrusions and benign network activity, thereby reducing false positives [151].