# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 2 Previous Work  
A CRN represents a transformative advancement in wireless communication technology; it can work with mobile technology and IoT systems. These networks leverage cognitive radio technology, allowing the devices to change their operating parameters based on the surrounding radio environment measures; devices continuously sense the spectrum frequencies, making them vulnerable to some attacks. Even if there are spectrum holes or white spaces, the SU needs to check the spectrum to prevent the PU affection; if there is a free space, it can use it for its purposes, taking advantage of dynamic spectrum management. However, intruders can use Denial of Service (DoS) attacks and interfere with a PU's communication services differently [6]. The PUE attacks are a type of DoS when an SU mimics the PU signal characteristics with two main objectives: selfish or malicious. Selfish is when it takes the spectrum for communications and malicious is when it gains spectrum resources to interrupt other SU or PU communications, acting like a jammer. The interference is so high that even the PU cannot use the spectrum even if they are paying for the license. The key to the attack being effective is to imitate the PU signal; depending on the service, it can be possible to delay discovering if there is a genuine PU or a PUE attack. The first attacks in the literature tended to increase the power, but attacks have changed this parameter to confuse the spectrum-sensing process [7]. Previous research into detecting PUE attacks in CRN has focused on distinguishing between legitimate PU and malicious users (MUs). The PU can be mobile, sensors, or IoT devices depending on the services. The emulators have been designed to capture the parameters or patterns of the signal and reproduce these later to gain access to the spectrum, with this being unauthorized access to the resources. In MCRN, network base stations are distributed and include critical user data of power, distance, or frequency. By capturing this information, the emulator creates a copy of it. To detect it, a series of techniques have been designed for the simplest to the most complex systems. AI techniques like K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANNs) are used to classify the users and determine the PUE attack. They depend on a database with user information and try to classify the attack by comparing the data. The channel is released if a PUE is detected, and other PUs or SUs can use it [8]. A PUE attack can also be addressed by disconnecting the MU from the base station with a classification process. The KNN classifier has been employed to identify the MU using data rate, distance from the base station, power received, and frequency, and the results are similar to those of an ANN trained with the same data. To increase security, Elliptic Curve Cryptography (ECC) is proposed for data encryption, showing high accuracy in detecting the PUE. In MCRN, base stations work with mobile subscribers or a PU to optimize the use of radio resources and manage frequency bands. PUEA detection and classification are performed at the base station, which maintains historical data about connected users. Various classifiers can be combined with a single-user energy detector. The KNN classifier is then used to analyze the behavior of the parameters, and network security is enhanced through ECC encryption [8]. For some PUE attackers, countermeasures using deep learning techniques have been developed, knowing that some information is needed for the training part of the algorithms. The authors use deep learning with the Extreme Value Theory (EVT) technique, which increases the detection of PUE attacks without previous information needed in the training part. This helps to discover the legitimate PU and recognize the MU in the network. It works in two parts: the first one needs to extract the features of the known PU, and the other analyzes the unknown user's features.