es vectors extracted by the Pattern Described Link-Signature (PDLS) method. By using this method, legitimate and malicious users could be effectively distinguished. When signal activity patterns can be regarded as a possible sequence relating to some "features" of the channel and previous internal states, the Recurrent Neural Network (RNN) is a good tool for PUE detection. An RNN-based PUE attack detection method was first introduced in [50]. It exploited series' temporal dependency for better series prediction and abnormal activity detection. To deal with the gradient vanishing issues inherent to RNN, an advanced version of RNN that took advantage of the Long Short Term Memory (LSTM) units and processed time series with long-term memory more efficiently was further proposed. It was shown that the LSTM-based method could significantly improve the detector's performance. The authors in [60] investigated the joint detection of PUE and jamming attacks in CRN. A sparse coding of the compressed received signal-based detection algorithm was proposed. Based on the channel dependent dictionary, convergence patterns in sparse coding were employed to differentiate the spectrum hole, legitimate PU, and emulators or jammers. An ML-based classification was adopted to perform the decision-making operation. The effec- tiveness and advantages of the proposed algorithm were verified in terms of the confusion matrix quality metric. By detecting PUE attacks and enhancing the probability of detection, a hybrid Genetic Artificial Bee Colony (GABC) algorithm was proposed in [52] to optimize spectrum utilization. A Genetic Algorithm was used to compensate for the Artificial Bee Colony algorithm's less than optimal exploitation of solutions by using crossover and mutation operations. The proposed GABC incorporated the Genetic operators into the Artificial Bee Colony algorithm to achieve balance between exploitation and exploration in order to find the optimal solution. 11.2.1.2 Robust Detection Methods Most of the classification approac