## 7 Overview of Deep Learning in 5G Security  
Deep learning is a subset of machine learning that leverages multiple layers of nonlinear processing units for feature extraction and transformation, learning multiple levels of representation from raw input data [9; 124]. These models are generally based on artificial neural networks, particularly CNNs and RNNs, and are designed to automatically and adaptively learn the spatial hierarchies of features. Within the realm of 5G security, deep learning brings about transformative potential with its capacity to analyze large datasets and recognize complex behavior and hidden patterns. DL algorithms are highly capable of detecting irregularities that could signal potential threats [101; 145; 146]. These abilities extend beyond mere detection, with algorithms capable of implementing defense mechanisms within milliseconds, a speed that is becoming increasingly critical in the hyper-connected 5G landscape. In addition to its superior processing abilities, DL also boasts self-learning capabilities, which are particularly beneficial for 5G networks that continuously generate a huge amount of data [132]. With traditional machine learning, the feature extraction process requires manual intervention, whereas deep learning models can learn these features directly from data, saving significant time and resources while also enhancing the accuracy of threat detection and response [124; 133]. However, despite the enormous potential that DL holds for 5G security, it is not without its challenges. These include computational intensity, difficulties in understanding how DL makes its decisions (i.e., black box problem), and the risk of adversarial attacks. In addition, [PERSON], [PERSON], [PERSON], [PERSON] [150] investigated the role of ML and DL algorithms in optimizing network data analytics for 5G cellular networks. Given the rise in connected devices and associated data, traditional analytics methods are proving insufficient. The authors highlighted the challenges faced, such as handling vast amounts of data and privacy concerns. They emphasized the efficacy of ML and DL in recognizing data patterns, which aids in real-time decisions, proactive management, and enhanced 5G network reliability. [PERSON] [106] examined mobile crowd sensing (MCS) systems that rely on public participation through their mobile devices for data collection. The central challenge is incentivizing participation. The authors introduced \"INCETION\", a unique MCS framework that integrates incentives with data aggregation and perturbation, ensuring both data accuracy and privacy. This integrated approach was validated through both theoretical and simulation-based methods. The evolution towards the 5G networks has significantly altered the telecommunication landscape, introducing unprecedented connectivity speed, massive network capacity, and reduced latency. However, the substantial benefits of 5G technology come with intricate security concerns primarily due to the massive scale, heterogeneity, and complexity of these networks. Therefore, it becomes imperative to leverage advanced, intelligent analytical tools that can adapt and respond to the ever-evolving security landscape. One such technology is DL, which has shown immense promise in cyber security applications. Deep learning, a subfield of machine learning, models high-level abstractions in data through the use of multiple layers of artificial neural networks [134]. What sets it apart from traditional machine learning is its ability to automatically learn representations from input data, eliminating the need for manual feature extraction. This self-learning capability is highly beneficial in a 5G context where networks continuously generate vast amounts of data. The capability to extract meaningful features from this sea of data allows DL algorithms to identify and react to complex patterns indicative of cyber threats [135].