diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..818433ecc0e094a4db1023c68b33f24344643ad8 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/LoRa_UAV_RFFI/.idea/LoRa_RFFI-main.iml b/LoRa_UAV_RFFI/.idea/LoRa_RFFI-main.iml new file mode 100644 index 0000000000000000000000000000000000000000..d563fab490bb5a5d4431ae8c2e821f54c91dce6c --- /dev/null +++ b/LoRa_UAV_RFFI/.idea/LoRa_RFFI-main.iml @@ -0,0 +1,22 @@ + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/LoRa_UAV_RFFI/.idea/inspectionProfiles/Project_Default.xml b/LoRa_UAV_RFFI/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000000000000000000000000000000000000..3dce9c67a3cba33789d113124d53150ccca2370b --- /dev/null +++ b/LoRa_UAV_RFFI/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,12 @@ + + + + \ No newline at end of file diff --git a/LoRa_UAV_RFFI/.idea/inspectionProfiles/profiles_settings.xml b/LoRa_UAV_RFFI/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000000000000000000000000000000000000..105ce2da2d6447d11dfe32bfb846c3d5b199fc99 --- /dev/null +++ b/LoRa_UAV_RFFI/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/LoRa_UAV_RFFI/.idea/misc.xml b/LoRa_UAV_RFFI/.idea/misc.xml new file mode 100644 index 0000000000000000000000000000000000000000..80571e70bea535862127a279d45cbe65e5a38481 --- /dev/null +++ b/LoRa_UAV_RFFI/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/LoRa_UAV_RFFI/.idea/modules.xml b/LoRa_UAV_RFFI/.idea/modules.xml new file mode 100644 index 0000000000000000000000000000000000000000..6180b98c03968706cc0f1487746610223a83628c --- /dev/null +++ b/LoRa_UAV_RFFI/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/LoRa_UAV_RFFI/.idea/other.xml b/LoRa_UAV_RFFI/.idea/other.xml new file mode 100644 index 0000000000000000000000000000000000000000..08305b919ef0d6b18ce3de4e4e73a442a2869043 --- /dev/null +++ b/LoRa_UAV_RFFI/.idea/other.xml @@ -0,0 +1,7 @@ + + + + + \ No newline at end of file diff --git a/LoRa_UAV_RFFI/.idea/workspace.xml b/LoRa_UAV_RFFI/.idea/workspace.xml new file mode 100644 index 0000000000000000000000000000000000000000..2bd03b37d6c7258dc61218072b16a99814b584ce --- /dev/null +++ b/LoRa_UAV_RFFI/.idea/workspace.xml @@ -0,0 +1,173 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1651942678596 + + + + + + + + + \ No newline at end of file diff --git a/LoRa_UAV_RFFI/__pycache__/dataset_preparation.cpython-37.pyc b/LoRa_UAV_RFFI/__pycache__/dataset_preparation.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..41ffb66eb90eb54874b29397ab4db115df1ba606 Binary files /dev/null and b/LoRa_UAV_RFFI/__pycache__/dataset_preparation.cpython-37.pyc differ diff --git a/LoRa_UAV_RFFI/__pycache__/deep_learning_models.cpython-37.pyc b/LoRa_UAV_RFFI/__pycache__/deep_learning_models.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e2a6810637bcda1548cdf3ff13408d46f6d412fa Binary files /dev/null and b/LoRa_UAV_RFFI/__pycache__/deep_learning_models.cpython-37.pyc differ diff --git a/LoRa_UAV_RFFI/dataset_preparation.py b/LoRa_UAV_RFFI/dataset_preparation.py new file mode 100644 index 0000000000000000000000000000000000000000..1838d3feead57e5138208d268e26f112eaab67f4 --- /dev/null +++ b/LoRa_UAV_RFFI/dataset_preparation.py @@ -0,0 +1,181 @@ +import numpy as np +import h5py +from numpy import sum,sqrt +from numpy.random import standard_normal, uniform + +from scipy import signal + +# In[] + +def awgn(data, snr_range): + + pkt_num = data.shape[0] + SNRdB = uniform(snr_range[0],snr_range[-1],pkt_num) + for pktIdx in range(pkt_num): + s = data[pktIdx] + # SNRdB = uniform(snr_range[0],snr_range[-1]) + SNR_linear = 10**(SNRdB[pktIdx]/10) + P= sum(abs(s)**2)/len(s) + N0=P/SNR_linear + n = sqrt(N0/2)*(standard_normal(len(s))+1j*standard_normal(len(s))) + data[pktIdx] = s + n + + return data + + + +class LoadDataset(): + def __init__(self,): + self.dataset_name = 'data' + self.labelset_name = 'label' + + def _convert_to_complex(self, data): + '''Convert the loaded data to complex IQ samples.''' + num_row = data.shape[0] + num_col = data.shape[1] + data_complex = np.zeros([num_row,round(num_col/2)],dtype=complex) + + data_complex = data[:,:round(num_col/2)] + 1j*data[:,round(num_col/2):] + return data_complex + + def load_iq_samples(self, file_path, dev_range, pkt_range): + ''' + Load IQ samples from a dataset. + + INPUT: + FILE_PATH is the dataset path. + + DEV_RANGE specifies the loaded device range. + + PKT_RANGE specifies the loaded packets range. + + RETURN: + DATA is the laoded complex IQ samples. + + LABLE is the true label of each received packet. + ''' + + f = h5py.File(file_path,'r') + label = f[self.labelset_name][:] + label = label.astype(int) + label = np.transpose(label) + label = label - 1 + + label_start = int(label[0]) + 1 + label_end = int(label[-1]) + 1 + num_dev = label_end - label_start + 1 + num_pkt = len(label) + num_pkt_per_dev = int(num_pkt/num_dev) + + print('Dataset information: Dev ' + str(label_start) + ' to Dev ' + + str(label_end) + ', ' + str(num_pkt_per_dev) + ' packets per device.') + + sample_index_list = [] + + for dev_idx in dev_range: + sample_index_dev = np.where(label==dev_idx)[0][pkt_range].tolist() + sample_index_list.extend(sample_index_dev) + + data = f[self.dataset_name][sample_index_list] + data = self._convert_to_complex(data) + + label = label[sample_index_list] + + f.close() + return data,label + + + +class ChannelIndSpectrogram(): + def __init__(self,): + pass + + def _normalization(self,data): + ''' Normalize the signal.''' + s_norm = np.zeros(data.shape, dtype=complex) + + for i in range(data.shape[0]): + + sig_amplitude = np.abs(data[i]) + rms = np.sqrt(np.mean(sig_amplitude**2)) + s_norm[i] = data[i]/rms + + return s_norm + + def _spec_crop(self, x): + '''Crop the generated channel independent spectrogram.''' + num_row = x.shape[0] + x_cropped = x[round(num_row*0.3):round(num_row*0.7)] + + return x_cropped + + + def _gen_single_channel_ind_spectrogram(self, sig, win_len=256, overlap=128): + ''' + _gen_single_channel_ind_spectrogram converts the IQ samples to a channel + independent spectrogram according to set window and overlap length. + + INPUT: + SIG is the complex IQ samples. + + WIN_LEN is the window length used in STFT. + + OVERLAP is the overlap length used in STFT. + + RETURN: + + CHAN_IND_SPEC_AMP is the genereated channel independent spectrogram. + ''' + # Short-time Fourier transform (STFT). + f, t, spec = signal.stft(sig, + window='boxcar', + nperseg= win_len, + noverlap= overlap, + nfft= win_len, + return_onesided=False, + padded = False, + boundary = None) + + # FFT shift to adjust the central frequency. + spec = np.fft.fftshift(spec, axes=0) + + # Generate channel independent spectrogram. + chan_ind_spec = spec[:,1:]/spec[:,:-1] + + # Take the logarithm of the magnitude. + chan_ind_spec_amp = np.log10(np.abs(chan_ind_spec)**2) + + return chan_ind_spec_amp + + + + def channel_ind_spectrogram(self, data): + ''' + channel_ind_spectrogram converts IQ samples to channel independent + spectrograms. + + INPUT: + DATA is the IQ samples. + + RETURN: + DATA_CHANNEL_IND_SPEC is channel independent spectrograms. + ''' + + # Normalize the IQ samples. + data = self._normalization(data) + + # Calculate the size of channel independent spectrograms. + num_sample = data.shape[0] + num_row = int(256*0.4) + num_column = int(np.floor((data.shape[1]-256)/128 + 1) - 1) + data_channel_ind_spec = np.zeros([num_sample, num_row, num_column, 1]) + + # Convert each packet (IQ samples) to a channel independent spectrogram. + for i in range(num_sample): + + chan_ind_spec_amp = self._gen_single_channel_ind_spectrogram(data[i]) + chan_ind_spec_amp = self._spec_crop(chan_ind_spec_amp) + data_channel_ind_spec[i,:,:,0] = chan_ind_spec_amp + + return data_channel_ind_spec + diff --git a/LoRa_UAV_RFFI/deep_learning_models.py b/LoRa_UAV_RFFI/deep_learning_models.py new file mode 100644 index 0000000000000000000000000000000000000000..862cb0b918468ab79d3be4f6e3a43c0d86d431f3 --- /dev/null +++ b/LoRa_UAV_RFFI/deep_learning_models.py @@ -0,0 +1,149 @@ +import numpy as np + + +from keras import backend as K +from keras.models import Model +from keras.layers import Input, Lambda, ReLU, Add, Dense, Conv2D, Flatten, AveragePooling2D + + +# In[] +def resblock(x, kernelsize, filters, first_layer = False): + + if first_layer: + fx = Conv2D(filters, kernelsize, padding='same')(x) + fx = ReLU()(fx) + fx = Conv2D(filters, kernelsize, padding='same')(fx) + + x = Conv2D(filters, 1, padding='same')(x) + + out = Add()([x,fx]) + out = ReLU()(out) + else: + fx = Conv2D(filters, kernelsize, padding='same')(x) + fx = ReLU()(fx) + fx = Conv2D(filters, kernelsize, padding='same')(fx) + + + out = Add()([x,fx]) + out = ReLU()(out) + + return out + +def identity_loss(y_true, y_pred): + return K.mean(y_pred) + + +class TripletNet(): + def __init__(self): + pass + + def create_triplet_net(self, embedding_net, alpha): + +# embedding_net = encoder() + self.alpha = alpha + + input_1 = Input([self.datashape[1],self.datashape[2],self.datashape[3]]) + input_2 = Input([self.datashape[1],self.datashape[2],self.datashape[3]]) + input_3 = Input([self.datashape[1],self.datashape[2],self.datashape[3]]) + + A = embedding_net(input_1) + P = embedding_net(input_2) + N = embedding_net(input_3) + + loss = Lambda(self.triplet_loss)([A, P, N]) + model = Model(inputs=[input_1, input_2, input_3], outputs=loss) + return model + + def triplet_loss(self,x): + # Triplet Loss function. + anchor,positive,negative = x +# K.l2_normalize + # distance between the anchor and the positive + pos_dist = K.sum(K.square(anchor-positive),axis=1) + # distance between the anchor and the negative + neg_dist = K.sum(K.square(anchor-negative),axis=1) + + basic_loss = pos_dist-neg_dist + self.alpha + loss = K.maximum(basic_loss,0.0) + return loss + + def feature_extractor(self, datashape): + + self.datashape = datashape + + inputs = Input(shape=([self.datashape[1],self.datashape[2],self.datashape[3]])) + + x = Conv2D(32, 7, strides = 2, activation='relu', padding='same')(inputs) + + x = resblock(x, 3, 32) + x = resblock(x, 3, 32) + + x = resblock(x, 3, 64, first_layer = True) + x = resblock(x, 3, 64) + + x = AveragePooling2D(pool_size=2)(x) + + x = Flatten()(x) + + x = Dense(512)(x) + + outputs = Lambda(lambda x: K.l2_normalize(x,axis=1))(x) + + model = Model(inputs=inputs, outputs=outputs) + return model + + + def get_triplet(self): + """Choose a triplet (anchor, positive, negative) of images + such that anchor and positive have the same label and + anchor and negative have different labels.""" + + + n = a = self.dev_range[np.random.randint(len(self.dev_range))] + + while n == a: + # keep searching randomly! + n = self.dev_range[np.random.randint(len(self.dev_range))] + a, p = self.call_sample(a), self.call_sample(a) + n = self.call_sample(n) + + return a, p, n + + + def call_sample(self,label_name): + """Choose an image from our training or test data with the + given label.""" + num_sample = len(self.label) + idx = np.random.randint(num_sample) + while self.label[idx] != label_name: + # keep searching randomly! + idx = np.random.randint(num_sample) + return self.data[idx] + + + def create_generator(self, batchsize, dev_range, data, label): + """Generate a triplets generator for training.""" + self.data = data + self.label = label + self.dev_range = dev_range + + while True: + list_a = [] + list_p = [] + list_n = [] + + for i in range(batchsize): + a, p, n = self.get_triplet() + list_a.append(a) + list_p.append(p) + list_n.append(n) + + A = np.array(list_a, dtype='float32') + P = np.array(list_p, dtype='float32') + N = np.array(list_n, dtype='float32') + + # a "dummy" label which will come in to our identity loss + # function below as y_true. We'll ignore it. + label = np.ones(batchsize) + yield [A, P, N], label + diff --git a/LoRa_UAV_RFFI/main.py b/LoRa_UAV_RFFI/main.py new file mode 100644 index 0000000000000000000000000000000000000000..121de4fb0a076bef0443fefe751ebb890d29b959 --- /dev/null +++ b/LoRa_UAV_RFFI/main.py @@ -0,0 +1,400 @@ +# 在import tensorflow之前 +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '-1' +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns + +from sklearn.metrics import roc_curve, auc, confusion_matrix, accuracy_score +from sklearn.neighbors import KNeighborsClassifier +from sklearn.model_selection import train_test_split + +from keras.models import load_model +from keras.callbacks import EarlyStopping, ReduceLROnPlateau +from keras.optimizers import RMSprop + +from dataset_preparation import awgn, LoadDataset, ChannelIndSpectrogram +from deep_learning_models import TripletNet, identity_loss + + +# %% + +def train_feature_extractor( + file_path='./dataset/Train/dataset_training_aug.h5', + dev_range=np.arange(0, 30, dtype=int), + pkt_range=np.arange(0, 1000, dtype=int), + snr_range=np.arange(20, 80) +): + ''' + + train_feature_extractor：使用三元组损失训练 RFF 提取器。 + + 输入： + FILE_PATH ：训练数据集的路径。 + + DEV_RANGE ： LoRa 设备的标签范围，用于训练 RFF 提取器。 + + PKT_RANGE ：来自每个 LoRa 设备的数据包范围，用于训练 RFF 提取器。 + + SNR_RANGE ：数据增强中使用的 SNR 范围。 + + 返回： + FEATURE_EXTRACTOR 是 由与信道无关的频谱图中训练的RFF提取器 + + ''' + + LoadDatasetObj = LoadDataset() + + # 加载前导 IQ 样本和标签 + data, label = LoadDatasetObj.load_iq_samples(file_path, + dev_range, + pkt_range) + + # 将加性高斯噪声添加到 IQ 样本 + data = awgn(data, snr_range) + + ChannelIndSpectrogramObj = ChannelIndSpectrogram() + + # 将时域 IQ 样本转换为与通道无关的频谱图 + data = ChannelIndSpectrogramObj.channel_ind_spectrogram(data) + + # 在训练期间指定超参数 + margin = 0.1 + batch_size = 32 + patience = 20 + + TripletNetObj = TripletNet() + + # 创建一个 RFF 提取器 + feature_extractor = TripletNetObj.feature_extractor(data.shape) + + # 使用 RFF 提取器创建 Triplet 网络 + triplet_net = TripletNetObj.create_triplet_net(feature_extractor, margin) + + # 在训练期间创建回调。当验证丢失时训练停止 + # 在 30 个 epoch 内不减少 + early_stop = EarlyStopping('val_loss', + min_delta=0, + patience= + patience) + + reduce_lr = ReduceLROnPlateau('val_loss', + min_delta=0, + factor=0.2, + patience=10, + verbose=1) + callbacks = [early_stop, reduce_lr] + + # 将数据集拆分为验证集和训练集 + data_train, data_valid, label_train, label_valid = train_test_split(data, + label, + test_size=0.1, + shuffle=True) + del data, label + + # 创建训练生成器 + train_generator = TripletNetObj.create_generator(batch_size, + dev_range, + data_train, + label_train) + # 创建验证生成器 + valid_generator = TripletNetObj.create_generator(batch_size, + dev_range, + data_valid, + label_valid) + + # 使用 RMSprop 优化器进行训练 + opt = RMSprop(learning_rate=1e-3) + triplet_net.compile(loss=identity_loss, optimizer=opt) + + # 开始训练 + history = triplet_net.fit(train_generator, + steps_per_epoch=data_train.shape[0] // batch_size, + epochs=10, + validation_data=valid_generator, + validation_steps=data_valid.shape[0] // batch_size, + verbose=1, + callbacks=callbacks) + + return feature_extractor + + +def test_classification( + file_path_enrol, + file_path_clf, + feature_extractor_name, + dev_range_enrol=np.arange(30, 40, dtype=int), + pkt_range_enrol=np.arange(0, 100, dtype=int), + dev_range_clf=np.arange(30, 40, dtype=int), + pkt_range_clf=np.arange(100, 200, dtype=int) +): + ''' + test_classification 执行分类任务并返回分类准确度。 + + 输入： + FILE_PATH_ENROL：注册数据集的路径。 + + FILE_PATH_CLF：分类数据集的路径。 + + FEATURE_EXTRACTOR_NAME：注册和分类期间使用的 RFF 提取器的名称。 + + DEV_RANGE_ENROL： 无人机LoRa设备在注册期间的标签范围。 + + PKT_RANGE_ENROL：注册期间来自每个 无人机LoRa 设备的数据包范围。 + + DEV_RANGE_CLF：LoRa设备在分类时的标签范围。 + + PKT_RANGE_CLF：分类期间来自每个 无人机LoRa 设备的数据包范围。 + + 返回： + PRED_LABEL 是预测标签的列表。 + + TRUE_LABEL 是列表真实标签。 + + ACC是整体分类准确率。 + ''' + + # 加载保存的 RFF 提取器 + feature_extractor = load_model(feature_extractor_name, compile=False) + + LoadDatasetObj = LoadDataset() + + # 加载注册数据集（IQ 样本和标签） + data_enrol, label_enrol = LoadDatasetObj.load_iq_samples(file_path_enrol, + dev_range_enrol, + pkt_range_enrol) + + ChannelIndSpectrogramObj = ChannelIndSpectrogram() + + # 将 IQ 样本转换为独立于通道的频谱图（注册数据） + data_enrol = ChannelIndSpectrogramObj.channel_ind_spectrogram(data_enrol) + + # # 可视化通道独立频谱图 + # plt.figure() + # sns.heatmap(data_enrol[0,:,:,0],xticklabels=[], yticklabels=[], cmap='Blues', cbar=False) + # plt.gca().invert_yaxis() + # plt.savefig('channel_ind_spectrogram.pdf') + + # 从独立于通道的频谱图中提取 RFF + feature_enrol = feature_extractor.predict(data_enrol) + del data_enrol + + # 使用从注册数据集中提取的 RFF 创建一个 K-NN 分类器 + knnclf = KNeighborsClassifier(n_neighbors=15, metric='euclidean') + knnclf.fit(feature_enrol, np.ravel(label_enrol)) + + # 加载分类数据集（IQ 样本和标签） + data_clf, true_label = LoadDatasetObj.load_iq_samples(file_path_clf, + dev_range_clf, + pkt_range_clf) + + # 将 IQ 样本转换为独立于通道的频谱图（分类数据） + data_clf = ChannelIndSpectrogramObj.channel_ind_spectrogram(data_clf) + + # 从独立于通道的频谱图中提取 RFF + feature_clf = feature_extractor.predict(data_clf) + del data_clf + + # 使用 K-NN 分类器进行预测 + pred_label = knnclf.predict(feature_clf) + + # 计算分类准确率 + acc = accuracy_score(true_label, pred_label) + print('Overall accuracy = %.4f' % acc) + + return pred_label, true_label, acc + + +def test_rogue_device_detection( + feature_extractor_name, + file_path_enrol='./dataset/Test/dataset_residential.h5', + dev_range_enrol=np.arange(30, 40, dtype=int), + pkt_range_enrol=np.arange(0, 100, dtype=int), + file_path_legitimate='./dataset/Test/dataset_residential.h5', + dev_range_legitimate=np.arange(30, 40, dtype=int), + pkt_range_legitimate=np.arange(100, 200, dtype=int), + file_path_rogue='./dataset/Test/dataset_rogue.h5', + dev_range_rogue=np.arange(40, 45, dtype=int), + pkt_range_rogue=np.arange(0, 100, dtype=int), +): + ''' + + test_rogue_device_detection 使用特定的 RFF 提取器执行恶意设备检测任务 + 它返回假阳性率 (FPR)、真阳性率 (TPR)、曲线下面积 (AUC) 和相应的阈值设置 + + 输入： + + FEATURE_EXTRACTOR_NAME：用于恶意设备检测的 RFF 提取器的名称 + + FILE_PATH_ENROL：注册数据集的路径 + + DEV_RANGE_ENROL：注册阶段使用的设备索引范围 + + PKT_RANGE_ENROL ：注册阶段使用的数据包索引范围 + + FILE_PATH_LEGITIMATE ：数据集的路径，其中包含来自合法设备的数据包 + + DEV_RANGE_LEGITIMATE ：在恶意设备检测阶段使用的合法设备的索引范围 + + PKT_RANGE_LEGITIMATE ：指定恶意设备检测阶段使用的合法设备的数据包范围 + + FILE_PATH_ROGUE ；数据集的路径，其中包含来自流氓设备的数据包 + + DEV_RANGE_ROGUE ：在恶意设备检测阶段使用的恶意设备的索引范围 + + PKT_RANGE_ROGUE ： 指定恶意设备检测阶段使用的恶意设备的数据包范围 + + 返回： + FPR是检测误报率 + + TRP是检测真阳性率 + + ROC_AUC 是 ROC 曲线下的面积 + + EER 是相等的错误率 + + ''' + + def _compute_eer(fpr, tpr, thresholds): + ''' + _COMPUTE_EER 返回相等的错误率 (EER) 和达到 EER 点的阈值 + ''' + fnr = 1 - tpr + abs_diffs = np.abs(fpr - fnr) + min_index = np.argmin(abs_diffs) + eer = np.mean((fpr[min_index], fnr[min_index])) + + return eer, thresholds[min_index] + + # 加载 RFF 提取器 + feature_extractor = load_model(feature_extractor_name, compile=False) + + LoadDatasetObj = LoadDataset() + + # 加载注册数据集 + data_enrol, label_enrol = LoadDatasetObj.load_iq_samples(file_path_enrol, + dev_range_enrol, + pkt_range_enrol) + + ChannelIndSpectrogramObj = ChannelIndSpectrogram() + + # 将 IQ 样本转换为独立于通道的频谱图 + data_enrol = ChannelIndSpectrogramObj.channel_ind_spectrogram(data_enrol) + + # 从与通道无关的频谱图中提取 RFF + feature_enrol = feature_extractor.predict(data_enrol) + del data_enrol + + # 构建一个 K-NN 分类器 + knnclf = KNeighborsClassifier(n_neighbors=15, metric='euclidean') + knnclf.fit(feature_enrol, np.ravel(label_enrol)) + + # 加载合法设备的测试数据集 + data_legitimate, label_legitimate = LoadDatasetObj.load_iq_samples(file_path_legitimate, + dev_range_legitimate, + pkt_range_legitimate) + # 加载恶意设备的测试数据集 + data_rogue, label_rogue = LoadDatasetObj.load_iq_samples(file_path_rogue, + dev_range_rogue, + pkt_range_rogue) + + # 将上述两个数据集合并为一个包含两个rogue的数据集 + # 以及合法设备的标签 + data_test = np.concatenate([data_legitimate, data_rogue]) + label_test = np.concatenate([label_legitimate, label_rogue]) + + # 将 IQ 样本转换为独立于通道的频谱图 + data_test = ChannelIndSpectrogramObj.channel_ind_spectrogram(data_test) + + # 从独立于通道的频谱图中提取 RFF + feature_test = feature_extractor.predict(data_test) + del data_test + + # 在 RFF 数据库中找到最近的 15 个邻居并计算距离 + distances, indexes = knnclf.kneighbors(feature_test) + + # 计算到最近 15 个邻居的平均距离 + detection_score = distances.mean(axis=1) + + # 将合法设备发送的报文标记为1，其余为流氓设备发送的，将其标记为0 + true_label = np.zeros([len(label_test), 1]) + true_label[(label_test <= dev_range_legitimate[-1]) & (label_test >= dev_range_legitimate[0])] = 1 + + # 计算ROC曲线 + fpr, tpr, thresholds = roc_curve(true_label, detection_score, pos_label=1) + + # 欧式距离用作检测分数。值越低，说明越接近越相似。 + # 这与 scikit-learn roc_curve 函数中使用的概率或置信度值相反。 + # 因此，我们需要从 1 中减去它们。 + fpr = 1 - fpr + tpr = 1 - tpr + + # 计算 EER. + eer, _ = _compute_eer(fpr, tpr, thresholds) + + # 计算 AUC. + roc_auc = auc(fpr, tpr) + + return fpr, tpr, roc_auc, eer + + +if __name__ == '__main__': + + # 指定程序运行的任务 + # 可选择'Train'/'Classification'/'Rogue Device Detection'三种任务 + run_for = 'Classification' + + if run_for == 'Train': + + # 训练 RFF 提取器 + feature_extractor = train_feature_extractor() + # 保存训练好的模型 + feature_extractor.save('Extractor.h5') + + + elif run_for == 'Classification': + + # 指定分类的设备索引范围 + test_dev_range = np.arange(30, 40, dtype=int) + + # 执行分类任务 + pred_label, true_label, acc = test_classification(file_path_enrol= + './dataset/Test/dataset_residential.h5', + file_path_clf= + './dataset/Test/channel_problem/A.h5', + feature_extractor_name= + './models/Extractor_1.h5') + + # 绘制混淆矩阵 + conf_mat = confusion_matrix(true_label, pred_label) + classes = test_dev_range + 1 + + plt.figure() + sns.heatmap(conf_mat, annot=True, + fmt='d', cmap='Blues', + cbar=False, + xticklabels=classes, + yticklabels=classes) + plt.xlabel('Predicted label', fontsize=20) + plt.ylabel('True label', fontsize=20) + plt.show() + + + elif run_for == 'Rogue Device Detection': + + # 使用三个 RFF 提取器执行恶意设备检测任务 + fpr, tpr, roc_auc, eer = test_rogue_device_detection('./models/Extractor_1.h5') + + # 绘制 ROC 曲线 + plt.figure(figsize=(4.8, 2.8)) + plt.xlim(-0.01, 1.02) + plt.ylim(-0.01, 1.02) + plt.plot([0, 1], [0, 1], 'k--') + plt.plot(fpr, tpr, label='Extractor 1, AUC = ' + + str(round(roc_auc, 3)) + ', EER = ' + str(round(eer, 3)), C='r') + plt.xlabel('False positive rate') + plt.ylabel('True positive rate') + plt.title('ROC curve') + plt.legend(loc=4) + # plt.savefig('roc_curve.pdf',bbox_inches='tight') + plt.show() diff --git a/LoRa_UAV_RFFI/readme.txt b/LoRa_UAV_RFFI/readme.txt new file mode 100644 index 0000000000000000000000000000000000000000..3ebb4e21ab5499a16a9f64fe551efa6069fdf2a9 --- /dev/null +++ b/LoRa_UAV_RFFI/readme.txt @@ -0,0 +1 @@ +数据集链接：https://ieee-dataport.org/open-access/lorarffidataset \ No newline at end of file diff --git a/LoRa_UAV_RFFI/requirements.txt b/LoRa_UAV_RFFI/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5b1f3f8fb8c18bfe287292dde0c07679c5ef63f --- /dev/null +++ b/LoRa_UAV_RFFI/requirements.txt @@ -0,0 +1,265 @@ +# This file may be used to create an environment using: +# $ conda create --name --file +# platform: win-64 +_tflow_select=2.1.0=gpu +absl-py=0.12.0=pyhd8ed1ab_0 +aiohttp=3.7.4=py36h68aa20f_0 +alabaster=0.7.12=py_0 +appdirs=1.4.4=pyh9f0ad1d_0 +argh=0.26.2=pyh9f0ad1d_1002 +arrow=1.1.0=pyhd8ed1ab_1 +astor=0.8.1=pyh9f0ad1d_0 +astroid=2.5.6=py36ha15d459_0 +async-timeout=3.0.1=py_1000 +async_generator=1.10=py_0 +atomicwrites=1.4.0=pyh9f0ad1d_0 +attrs=21.2.0=pyhd8ed1ab_0 +autopep8=1.5.6=pyhd8ed1ab_0 +babel=2.9.1=pyh44b312d_0 +backcall=0.2.0=pyh9f0ad1d_0 +backports=1.0=py_2 +backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0 +bcrypt=3.2.0=py36h68aa20f_1 +binaryornot=0.4.4=py_1 +black=21.5b0=pyhd8ed1ab_0 +blas=1.0=mkl +bleach=3.3.0=pyh44b312d_0 +blinker=1.4=py_1 +brotlipy=0.7.0=py36h68aa20f_1001 +ca-certificates=2021.5.30=h5b45459_0 +cachetools=4.2.2=pyhd8ed1ab_0 +certifi=2021.5.30=py36ha15d459_0 +cffi=1.14.5=py36he58ceb7_0 +chardet=4.0.0=py36ha15d459_1 +click=8.0.1=py36ha15d459_0 +cloudpickle=1.6.0=py_0 +colorama=0.4.4=pyh9f0ad1d_0 +cookiecutter=1.7.0=py_0 +cryptography=3.4.7=py36hd0de82c_0 +cudatoolkit=10.1.243=h3826478_8 +cudnn=7.6.5.32=h36d860d_1 +cycler=0.10.0=pypi_0 +dataclasses=0.8=pyh787bdff_0 +decorator=5.0.9=pyhd8ed1ab_0 +defusedxml=0.7.1=pyhd8ed1ab_0 +diff-match-patch=20200713=pyh9f0ad1d_0 +docutils=0.17.1=py36ha15d459_0 +entrypoints=0.3=pyhd8ed1ab_1003 +flake8=3.8.4=py_0 +freetype=2.10.4=h546665d_1 +future=0.18.2=py36ha15d459_3 +gast=0.2.2=py_0 +google-auth=1.30.0=pyh44b312d_0 +google-auth-oauthlib=0.4.1=py_2 +google-pasta=0.2.0=pyh8c360ce_0 +grpcio=1.37.1=py36h4374274_0 +h5py=2.10.0=nompi_py36h6cf6063_105 +hdf5=1.10.6=nompi_h5268f04_1114 +icu=68.1=h0e60522_0 +idna=2.10=pyh9f0ad1d_0 +idna_ssl=1.1.0=py36h9f0ad1d_1001 +imagesize=1.2.0=py_0 +importlib-metadata=4.0.1=py36ha15d459_0 +importlib_metadata=4.0.1=hd8ed1ab_0 +inflection=0.5.1=pyh9f0ad1d_0 +intel-openmp=2021.2.0=h57928b3_616 +intervaltree=3.0.2=py_0 +ipykernel=5.5.5=py36hfacbf0b_0 +ipython=7.16.1=py36h7b2dad6_2 +ipython_genutils=0.2.0=py_1 +isort=5.8.0=pyhd8ed1ab_0 +jbig=2.1=h8d14728_2003 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+llvmlite=0.36.0=py36haecd60e_0 +lz4-c=1.9.3=h8ffe710_0 +m2w64-gcc-libgfortran=5.3.0=6 +m2w64-gcc-libs=5.3.0=7 +m2w64-gcc-libs-core=5.3.0=7 +m2w64-gmp=6.1.0=2 +m2w64-libwinpthread-git=5.0.0.4634.697f757=2 +mako=1.1.4=pyh44b312d_0 +markdown=3.3.4=pyhd8ed1ab_0 +markupsafe=2.0.1=py36h68aa20f_0 +matplotlib=3.3.3=pypi_0 +mccabe=0.6.1=py_1 +mistune=0.8.4=py36h68aa20f_1003 +mkl=2020.4=hb70f87d_311 +mkl-include=2021.2.0=hb70f87d_389 +msys2-conda-epoch=20160418=1 +multidict=5.1.0=py36h68aa20f_1 +mypy_extensions=0.4.3=py36ha15d459_3 +natsort=7.0.1=py_0 +nbclient=0.5.3=pyhd8ed1ab_0 +nbconvert=6.0.7=py36ha15d459_3 +nbformat=5.1.3=pyhd8ed1ab_0 +nest-asyncio=1.5.1=pyhd8ed1ab_0 +ninja=1.10.2=h5362a0b_0 +numba=0.53.1=py36h79ea69f_0 +numpy=1.19.5=py36hd1b969e_1 +numpydoc=1.1.0=py_1 +oauthlib=3.0.1=py_0 +olefile=0.46=pyh9f0ad1d_1 +openjpeg=2.4.0=hb211442_1 +openssl=1.1.1k=h8ffe710_0 +opt_einsum=3.3.0=pyhd8ed1ab_1 +packaging=20.9=pyh44b312d_0 +pandas=1.1.5=pypi_0 +pandoc=2.13=h8ffe710_0 +pandocfilters=1.4.2=py_1 +paramiko=2.7.2=pyh9f0ad1d_0 +parso=0.7.0=pyh9f0ad1d_0 +pathspec=0.8.1=pyhd3deb0d_0 +pexpect=4.8.0=pyh9f0ad1d_2 +pickleshare=0.7.5=py_1003 +pillow=8.0.1=pypi_0 +pip=21.1.1=pyhd8ed1ab_0 +pluggy=0.13.1=py36ha15d459_4 +poyo=0.5.0=py_0 +prompt-toolkit=3.0.18=pyha770c72_0 +protobuf=3.17.0=py36he2d232f_0 +psutil=5.8.0=py36h68aa20f_1 +ptyprocess=0.7.0=pyhd3deb0d_0 +pyasn1=0.4.8=py_0 +pyasn1-modules=0.2.7=py_0 +pycodestyle=2.6.0=pyh9f0ad1d_0 +pycparser=2.20=pyh9f0ad1d_2 +pydocstyle=6.1.1=pyhd8ed1ab_0 +pyflakes=2.2.0=pyh9f0ad1d_0 +pygments=2.9.0=pyhd8ed1ab_0 +pygpu=0.7.6=py36h6434af4_1002 +pyjwt=2.1.0=pyhd8ed1ab_0 +pylint=2.7.2=py36ha15d459_0 +pyls-black=0.4.6=pyh9f0ad1d_0 +pyls-spyder=0.3.2=pyhd8ed1ab_0 +pynacl=1.4.0=py36hb5e345e_2 +pyopenssl=20.0.1=pyhd8ed1ab_0 +pyparsing=2.4.7=pyh9f0ad1d_0 +pyqt=5.12.3=py36ha15d459_7 +pyqt-impl=5.12.3=py36he2d232f_7 +pyqt5-sip=4.19.18=py36he2d232f_7 +pyqtchart=5.12=py36he2d232f_7 +pyqtwebengine=5.12.1=py36he2d232f_7 +pyreadline=2.1=py36ha15d459_1003 +pyrsistent=0.17.3=py36h68aa20f_2 +pyrtlsdr=0.2.92=pypi_0 +pysocks=1.7.1=py36ha15d459_3 +python=3.6.12=h39d44d4_0_cpython +python-dateutil=2.8.1=py_0 +python-jsonrpc-server=0.4.0=pyh9f0ad1d_0 +python-language-server=0.36.2=pyhd8ed1ab_0 +python_abi=3.6=1_cp36m +pytorch=1.8.1=py3.6_cuda10.1_cudnn7_0 +pytz=2021.1=pyhd8ed1ab_0 +pywin32=300=py36h68aa20f_0 +pywin32-ctypes=0.2.0=py36ha15d459_1003 +pyyaml=5.3.1=pypi_0 +pyzmq=22.0.3=py36h1d5d788_1 +qdarkstyle=3.0.2=pyhd8ed1ab_0 +qstylizer=0.2.0=pyhd8ed1ab_1 +qt=5.12.9=h5909a2a_4 +qtawesome=1.0.2=pyhd8ed1ab_0 +qtconsole=5.1.0=pyhd8ed1ab_0 +qtpy=1.9.0=py_0 +regex=2021.4.4=py36h68aa20f_0 +requests=2.25.1=pyhd3deb0d_0 +requests-oauthlib=1.3.0=pyh9f0ad1d_0 +rope=0.18.0=pyhd3deb0d_0 +rsa=4.7.2=pyh44b312d_0 +rtree=0.9.4=py36h089df06_2 +scikit-learn=0.23.2=pypi_0 +scipy=1.4.1=pypi_0 +seaborn=0.11.1=pypi_0 +setuptools=49.6.0=py36ha15d459_3 +six=1.16.0=pyh6c4a22f_0 +sklearn=0.0=pypi_0 +snowballstemmer=2.1.0=pyhd8ed1ab_0 +sortedcontainers=2.4.0=pyhd8ed1ab_0 +sphinx=4.0.1=pyh6c4a22f_1 +sphinxcontrib-applehelp=1.0.2=py_0 +sphinxcontrib-devhelp=1.0.2=py_0 +sphinxcontrib-htmlhelp=1.0.3=py_0 +sphinxcontrib-jsmath=1.0.1=py_0 +sphinxcontrib-qthelp=1.0.3=py_0 +sphinxcontrib-serializinghtml=1.1.4=py_0 +spyder=5.0.2=py36ha15d459_0 +spyder-kernels=2.0.3=py36ha15d459_0 +sqlite=3.35.5=h8ffe710_0 +stft=0.5.2=pypi_0 +tensorboard=2.1.1=pypi_0 +tensorboard-plugin-wit=1.8.0=pyh44b312d_0 +tensorflow=2.1.0=gpu_py36h3346743_0 +tensorflow-base=2.1.0=gpu_py36h55f5790_0 +tensorflow-estimator=2.1.0=pyhd54b08b_0 +tensorflow-gpu=2.1.0=h0d30ee6_0 +termcolor=1.1.0=py_2 +testpath=0.5.0=pyhd8ed1ab_0 +textdistance=4.2.1=pyhd8ed1ab_0 +theano=1.0.5=py36he2d232f_1 +threadpoolctl=2.1.0=pypi_0 +three-merge=0.1.1=pyh9f0ad1d_0 +tinycss2=1.1.0=pyhd8ed1ab_0 +tk=8.6.10=h8ffe710_1 +toml=0.10.2=pyhd8ed1ab_0 +torchaudio=0.8.1=py36 +torchvision=0.2.2=py_3 +tornado=6.1=py36h68aa20f_1 +traitlets=4.3.3=py36h9f0ad1d_1 +typed-ast=1.4.3=py36h68aa20f_0 +typing-extensions=3.7.4.3=0 +typing_extensions=3.7.4.3=py_0 +ujson=4.0.2=py36he2d232f_0 +urllib3=1.26.4=pyhd8ed1ab_0 +vc=14.2=hb210afc_4 +vs2015_runtime=14.28.29325=h5e1d092_4 +vs2017_win-64=19.16.27038=h2e3bad8_2 +vswhere=2.8.4=h57928b3_0 +watchdog=1.0.2=py36ha15d459_1 +wcwidth=0.2.5=pyh9f0ad1d_2 +webencodings=0.5.1=py_1 +werkzeug=0.16.1=py_0 +wheel=0.36.2=pyhd3deb0d_0 +whichcraft=0.6.1=py_0 +win_inet_pton=1.1.0=py36ha15d459_2 +wincertstore=0.2=py36ha15d459_1006 +wrapt=1.12.1=py36h68aa20f_3 +xz=5.2.5=h62dcd97_1 +yaml=0.2.5=he774522_0 +yapf=0.31.0=pyhd8ed1ab_0 +yarl=1.6.3=py36h68aa20f_1 +zeromq=4.3.4=h0e60522_0 +zipp=3.4.1=pyhd8ed1ab_0 +zlib=1.2.11=h62dcd97_1010 +zstd=1.5.0=h6255e5f_0 diff --git a/LoRa_UAV_RFFI/test.py b/LoRa_UAV_RFFI/test.py new file mode 100644 index 0000000000000000000000000000000000000000..6858cffa2c84006a47b9daf04fd0f7affaf3d186 --- /dev/null +++ b/LoRa_UAV_RFFI/test.py @@ -0,0 +1,8 @@ +import tensorflow as tf +import os +os.environ['TF_CPP_MIN_LOG_LEVEL']='2' +print(tf.__version__) +a = tf.constant(1.) +b = tf.constant(2.) +print(a+b) +print('GPU:', tf.test.is_gpu_available())