# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 2 Previous Work
There is a dependence on EVT and Weibull distributions to make the decisions, and it needs to know the PU parameters for the classification process [9]. Another way to address a PUEA is to explore intruder classification and focus on its impact on the CRN system. It uses a two-level auxiliary database that, as an input, has the energy consumption and verifies the device's location. It uses a method to reserve some channels for handover. Another intrusion detection uses the real PU locations and the received power. This traditional technique is based on a fixed PU position but does not work with a mobile user. It analyzes the signal parameters, calculates the energy based on the power, and locates the user with the help of a sensor network, indicating the need for some additional nodes to calculate it. Then, it decides whether a PUE is present in the network. It needs the precise position of the PU to operate adequately, and it does not work with an MCRN. Based on this, a practical machine-learning-based intrusion detection approach is used with some additional nodes using a comparison based on accuracy and precision and showing a high performance compared to previous techniques [10]. Two strategies have been used for spectrum sensing in the presence of PUE attacks: local sensing and the CSS. Local sensing can use match filter, energy, features, eigenvalue, or wavelet detection, while the CSS is based on centralized, distributed, or relay-assisted cooperation. The centralized base station or fusion center is used to communicate with all the SUs through a common control channel and share global decisions with all the SUs, decreasing the impact of the hidden node; this solution is used with the MCRN. In the distributed, it only communicates with another node to help in the decision, and the relay assistant uses a cognitive user to sense the channel and use an independent channel to communicate, helping in the decision of the PUE presence [7]. Previous work has highlighted that the requirement for feature extraction in traditional machine learning techniques can limit the full potential of raw data. Then, a framework with one-dimensional deep learning is used to identify PUE attacks. It uses a CR environment with a PU node, an SU node, and a PUE attacker. This allows for spectrum exploitation if there is a frequency space with the PUE. By implementing a one-dimensional CNN (1D-CNN), there is no need for explicit feature extraction. It enhances detection performance for PUE and jamming attacks (JAs), eliminating the dependence on feature extraction for PUE detection. The performance is evaluated by analyzing the ROC curves of the false alarm probability, and the results are better than those of the feature characteristics selection technique [6]. Recurrent Neural Networks (RNNs) have been used to detect PUE attacks when signal activity patterns can be recognized or associated with PU signal behavior, and it depends on past states. It leverages the temporal dependencies in time series data to increase prediction accuracy and identify suspicious activities. To address the gradient vanishing problem associated with RNNs, an advanced RNN variant incorporating Long Short-Term Memory (LSTM) units was introduced. This improves the analysis and processing of large datasets and significantly increases the performance of PUE attack detectors [11]. Another approach has focused on improving the security of the cognitive sensing part process using a hybrid Genetic Artificial Bee Colony (GABC) algorithm. This is useful for detecting PUE and shows a promising increase in detection performance. It combines Genetic Algorithm (GA) operators with the Artificial Bee Colony (ABC) algorithm, refining the detection process and increasing the likelihood of accurate attack identification. It balances exploration and exploitation, seeking an optimal solution and optimizing spectrum sensing [12]. In mobile networks, the challenge of managing connections among numerous CR-enabled 5G massive machine-type communications (mMTC) devices was studied.