With the emergence of new technologies and a growing number of wireless networks, we face the problem of radio spectrum shortages. As a result, identifying the wireless channel spectrum to exploit the channel's idle state while also boosting network security is a pivotal issue. Detecting and classifying protocols in the MAC sublayer enables Cognitive Radio users to improve spectrum utilization and minimize potential interference. In this paper, we classify the Wi-Fi and Bluetooth protocols, which are the most widely used MAC sublayer protocols in the ISM radio band. With the advent of various wireless technologies, especially in the 2.4 GHz frequency band, the ISM frequency spectrum has become crowded and high-traffic, which faces a lack of spectrum resources and user interference. Therefore, identifying and classifying protocols is an effective and useful method. Leveraging machine learning and deep learning techniques, known for their advanced classification capabilities, we apply Support Vector Machine and K-Nearest Neighbors algorithms, which are machine learning algorithms, to classify protocols into three classes: Wi-Fi, Wi-Fi Beacon, and Bluetooth. To capture the signals, we use the USRP N210 Software Defined Radio device and sample the real data in the indoor environment in different conditions of the presence and absence of transmitters and receivers for these two protocols. By assembling this dataset and studying the time and frequency features of the protocols, we extract the frame width and the silence gap between the two frames as time features and the PAPR of each frame as a power feature. By comparing the output of the protocols classification in different conditions and also adding Gaussian noise, it was found that the samples in the nonlinear SVM method with RBF and KNN functions have the best performance, with 97.83% and 98.12% classification accuracy, respectively.

翻译：随着新技术的涌现和无线网络数量的不断增长，我们面临着无线电频谱短缺的问题。因此，识别无线信道频谱以利用信道空闲状态并同时提升网络安全性成为一个关键议题。在MAC子层检测和分类协议能够使认知无线电用户提高频谱利用率并最小化潜在干扰。本文对ISM无线电频段中最广泛使用的MAC子层协议——Wi-Fi和蓝牙协议——进行分类。随着各种无线技术的出现，特别是在2.4 GHz频段，ISM频谱已变得拥挤且流量密集，面临着频谱资源不足和用户干扰的问题。因此，识别和分类协议成为一种有效且实用的方法。利用以先进分类能力著称的机器学习和深度学习技术，我们应用支持向量机和K近邻算法这两种机器学习算法，将协议分为三类：Wi-Fi、Wi-Fi信标和蓝牙。为捕获信号，我们使用USRP N210软件定义无线电设备，并在室内环境中针对这两种协议在不同发射器和接收器存在与否的条件下采集真实数据。通过构建该数据集并研究协议的时频特征，我们提取帧宽度和两帧间的静默间隔作为时间特征，以及每帧的峰均功率比作为功率特征。通过比较不同条件下协议分类的输出结果，并添加高斯噪声后发现，采用RBF核函数的非线性SVM方法和KNN方法的样本具有最佳性能，分类准确率分别达到97.83%和98.12%。