# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 6 Discussion  
The CSS single energy detector working with an authenticator detector can detect a PUE attack with a probability of detection above 90% for an SNR of \(-\)10 dB in simulations. In the SDR experiments with the DNN, this value is achieved in \(-\)9 dB, very close to simulations, and works with a mobile PUFA and PU. In the presence of four PUE attackers in the environment, the SU starts releasing frequencies and detecting the possible PUE, obtaining a 90% detection in \(-\)7 dB. The effects of multiple PUEs are an increasing value of SNR, and the time detection increases by 2.36 s, the average time the DNN algorithm takes to detect the attack. The DNN algorithm works faster with the energy detector to release the channel and detect the malicious PUE attackers; authentication detection takes an average time of 20 s, obtains 90% of detection, and is also used to save the data from the attacker and update the database from PU and PUE attackers. The DNN algorithm detects multiple PUE attackers even if they are simultaneous in 5 s, but it releases the channel in 200 ms.  
The cognitive protocol implemented in this project is a CSS with an FC that keeps all the network data; it has been probed with users in movement in the MCRN in a controlled environment. To adapt it to a real-world scenario, continuous monitoring of the spectrum environment and user mobility is needed to adjust parameters like frequency allocation and power control dynamically. Given the potential for various interference sources, the protocol must be designed to maintain robustness. Adaptive modulation, error correction, and interference mitigation strategies can help sustain communication quality under diverse conditions.  
As part of future work, the protocol should be tested in scenarios that simulate large-scale deployments with increasing user density. This includes implementing resource management strategies that can efficiently handle the demands of multiple users while minimizing conflicts and optimizing spectrum usage. Implementing cooperative strategies among SUs can enhance detection and response to PUE attacks. A distributed learning approach, where devices share information about detected attacks, can improve system awareness and responsiveness.  
Integrating deep neural networks (DNNs) can help detect PUE attacks and predict network conditions. Establishing feedback loops where SUs can report their experiences with interference and PUE attacks will create a learning environment that continuously improves the protocol's performance. This approach enhances the protocol's robustness and adaptability and supports the collaborative nature of Mobile Cognitive Radio Networks (MCRNs).  
As the SUs increase, the FC may struggle to process incoming data efficiently. In future work, a hybrid solution that combines centralized and decentralized elements will be explored to leverage the strengths of both architectures.  
Using the DNN improves the prediction rates, decreases the false alarm rates, and speeds up the detection process in the presence of multiple or single PUE attacks. It can process several amounts of data in the learning process and extract the features or patterns of the inputs, simplifying the modeling process and adapting to one or multiple PUE attacks.