# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 3 Deep Neural Network and Entropy to Multiple PUE Detection in Cooperative MCRN
### Deep Learning Techniques for the Decision of PUE Presence  
Deep learning is one of the most effective tools for making accurate forecasts using large and complex data sets. Deep learning includes technologies with multiple data types, including numbers, text, audio, images, video, or combinations [22]. We propose to use one of the deep learning techniques to help the SU detect the _PUE attack_; it must be implemented on each SU to improve the decision in a CSS scheme. In this section, an explanation of the context and deep learning techniques is given, and after this, we propose a deep learning scheme for detecting the _PUE attack_. The central concept of deep learning is the use of neural networks, and even though their applications are widely used today, this technology is familiar, having emerged in the 1950s. However, its development was limited after the 1970s due to a lack of hardware and algorithms necessary to achieve the great expectations generated around neural networks. AI has been used in many fields, such as robots, mobile communications, autonomous cars, smart cities, financial analysis, and engineering problems such as decision support [23]. In 2012, there was a significant advance in the field of artificial intelligence, which allowed the creation of neural networks capable of identifying images with greater precision than humans. Later AI examples include Deep Blue, a chess-playing system that defeated [PERSON], or Google's AlphaGo, which defeated the world's No. 1 ranked player [PERSON] in a Go match. In 2022, OpenAI launched Chat Generative Pre-trained Transformer (ChatGPT), a next-generation model that generates human-like responses based on text input [23]. In this case, we will use the neural networks for decision-making in an MCRN under a _PUE attack_. To decide which neural network is needed, the ANN concept is analyzed. The fundamental components of the nervous system in biological systems are neurons; the complexity of this system derives from the millions of neuron connections when they communicate with each other through axons and dendrites. When a living organism learns, connections between neurons are established through a process called synapses [22]. Artificial neural networks are inspired by biological behavior through artificial neurons that communicate with each other and can learn from data. A neuron performs a linear regression from a set of weighted inputs and a bias [23]. There are different types of neural networks, each with its own architecture [24]. * Deep or fully connected networks (DNN): These are networks in which each neuron of a layer is connected with all the neurons of the subsequent layer; they have been used mainly in regression problems and the classification of supervised learning [25]. * Convolutional Networks (CNN): These networks, in which a set of filters is implemented in each layer, are very efficient in image processing. * Recurrent Networks (RNN): In these models, the output depends on the current input and the information processed in the last moments. This characteristic, called short- and long-term memory, allows them to work with data series such as text, audio, or video. They are often used in natural language processing tasks, voice recognition, and time series analysis, among other applications [24]. * Generative Adversarial Networks (GAN): They are a system comprising two neural networks: generative and discriminative. The generative network creates new data from the training data set. In contrast, the discriminative network judges whether the data it receives is training data or data created by the generative network. As this competition process between the two networks progresses, each performs its task more efficiently. Thanks to this architecture, GANs have proven capable of generating high-quality images, music, and videos [26]. Deep neural networks are an effective tool for classification or regression tasks. These networks can identify complex patterns that relate input data to output values.