使用VAE、CNN encoder+孤立森林检测ssl加密异常流的初探——真是一个忧伤的故事！！！

ssl payload取1024字节，然后使用VAE检测异常的ssl流。

代码如下：

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import tensorflow as tf
import tflearn
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
import pandas as pd
from sklearn.metrics import average_precision_score, recall_score, precision_score, f1_score
import os
from PIL import Image


def report_evaluation_metrics(y_true, y_pred):
    average_precision = average_precision_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred, labels=[0, 1], pos_label=1)
    recall = recall_score(y_true, y_pred, labels=[0, 1], pos_label=1)
    f1 = f1_score(y_true, y_pred, labels=[0, 1], pos_label=1)

    print('Average precision-recall score: {0:0.2f}'.format(average_precision))
    print('Precision: {0:0.2f}'.format(precision))
    print('Recall: {0:0.2f}'.format(recall))
    print('F1: {0:0.2f}'.format(f1))


LABELS = ["Normal", "Fraud"]


def plot_confusion_matrix(y_true, y_pred):
    conf_matrix = confusion_matrix(y_true, y_pred)

    plt.figure(figsize=(12, 12))
    sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt="d")
    plt.title("Confusion matrix")
    plt.ylabel('True class')
    plt.xlabel('Predicted class')
    plt.show()


def plot_training_history(history):
    if history is None:
        return
    plt.plot(history['loss'])
    plt.plot(history['val_loss'])
    plt.title('model loss')
    plt.ylabel('loss')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper right')
    plt.show()


def visualize_anomaly(y_true, reconstruction_error, threshold):
    error_df = pd.DataFrame({'reconstruction_error': reconstruction_error,
                             'true_class': y_true})
    print(error_df.describe())

    groups = error_df.groupby('true_class')
    fig, ax = plt.subplots()

    for name, group in groups:
        ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='',
                label="Fraud" if name == 1 else "Normal")

    ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors="r", zorder=100, label='Threshold')
    ax.legend()
    plt.title("Reconstruction error for different classes")
    plt.ylabel("Reconstruction error")
    plt.xlabel("Data point index")
    plt.show()


def visualize_reconstruction_error(reconstruction_error, threshold):
    plt.plot(reconstruction_error, marker='o', ms=3.5, linestyle='',
             label='Point')

    plt.hlines(threshold, xmin=0, xmax=len(reconstruction_error) - 1, colors="r", zorder=100, label='Threshold')
    plt.legend()
    plt.title("Reconstruction error")
    plt.ylabel("Reconstruction error")
    plt.xlabel("Data point index")
    plt.show()


def get_images():
    image_list = []
    files = []
    cnt = 0
    img_dir = "png2"
    for file in os.listdir(img_dir):
        path = os.path.join(img_dir, file)
        if not os.path.isfile(path):
            print("{} is not a file!!!".format(path))
            continue
        cnt += 1
        temp_image = Image.open(path).convert('L')
        # temp_image = temp_image.resize((32, 32), Image.ANTIALIAS)
        temp_image = np.asarray(temp_image) / 255.0
        image_list.append(temp_image)
        files.append(file)
    image_list = np.asarray(image_list)
    input_image = image_list.reshape([cnt, 32, 32, 1])
    return input_image, np.array(files)


def preprocess_data(csv_data):
    credit_card_data = csv_data.drop(labels=['Class', 'Time'], axis=1)
    credit_card_data['Amount'] = StandardScaler().fit_transform(credit_card_data['Amount'].values.reshape(-1, 1))
    # print(credit_card_data.head())
    credit_card_np_data = credit_card_data.as_matrix()
    y_true = csv_data['Class'].as_matrix()
    return credit_card_np_data, y_true


# encoder
def encode(input_x, encoder_hidden_dim, latent_dim):
    """
    # keras
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
x = Dense(intermediate_dim, activation='relu')(inputs)
z_mean = Dense(latent_dim, name='z_mean')(x)
z_log_var = Dense(latent_dim, name='z_log_var')(x)
    """
    encoder = tflearn.fully_connected(input_x, encoder_hidden_dim, activation='relu')
    mu_encoder = tflearn.fully_connected(encoder, latent_dim, activation='linear')
    logvar_encoder = tflearn.fully_connected(encoder, latent_dim, activation='linear')
    return mu_encoder, logvar_encoder


# decoder
def decode(z, decoder_hidden_dim, input_dim):
    """
# build decoder model
latent_inputs = Input(shape=(latent_dim,), name='z_sampling')
x = Dense(intermediate_dim, activation='relu')(latent_inputs)
outputs = Dense(original_dim, activation='sigmoid')(x)
    """
    decoder = tflearn.fully_connected(z, decoder_hidden_dim, activation='relu')
    x_hat = tflearn.fully_connected(decoder, input_dim, activation='linear')
    return x_hat


# sampler
def sample(mu, logvar):
    """
    keras
    z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
    # reparameterization trick
# instead of sampling from Q(z|X), sample eps = N(0,I)
# z = z_mean + sqrt(var)*eps
def sampling(args):
    z_mean, z_log_var = args
    batch = K.shape(z_mean)[0]
    dim = K.int_shape(z_mean)[1]
    # by default, random_normal has mean=0 and std=1.0
    epsilon = K.random_normal(shape=(batch, dim))
    return z_mean + K.exp(0.5 * z_log_var) * epsilon
    """
    epsilon = tf.random_normal(tf.shape(logvar), dtype=tf.float32, name='epsilon')
    # std_encoder = tf.exp(tf.mul(0.5, logvar))
    # z = tf.add(mu, tf.mul(std_encoder, epsilon))
    z = mu + tf.exp(logvar / 2) * epsilon
    return z


# loss function(regularization)
def calculate_regularization_loss(mu, logvar):
    kl_divergence = -0.5 * tf.reduce_sum(1 + logvar - tf.square(mu) - tf.exp(logvar), reduction_indices=1)
    return kl_divergence


# loss function(reconstruction)
def calculate_reconstruction_loss(x_hat, input_x):
    mse = tflearn.objectives.mean_square(x_hat, input_x)
    return mse


def main():
    anomaly_ratio = 0.0001
    estimated_negative_sample_ratio = 1 - anomaly_ratio
    print(estimated_negative_sample_ratio)

    data_file = "data.npz"
    if os.path.exists(data_file):
        print("load data file data.npz!!!")
        data = np.load(data_file)
        X, files = data['X'], data['files']
    else:
        X, files = get_images()
        np.savez(data_file, X=X, files=files)

    X = X.reshape([len(X), 32*32])

    trainX, testX, trainY, testY = train_test_split(X, X, test_size=0.05, random_state=42)


    print("sample data: X:{} ".format(X[:3]))
    print(X.shape)

    # detect anomaly for the test data
    Ypred = []

    # blackY_indices = np.where(Y)[0]
    # print(blackY_indices[:3], "sample fraud credit data")
    # assert Y[blackY_indices[0]]
    # assert Y[blackY_indices[-1]]

    # X, Y, testX, testY = mnist.load_data(one_hot=True)

    # Params
    original_dim = len(X[0])  # MNIST images are 28x28 pixels
    print("dim: {}".format(original_dim))

    """
    # Building the encoder
    encoder = tflearn.input_data(shape=[None, original_dim])
    encoder = tflearn.fully_connected(encoder, 8)
    encoder = tflearn.fully_connected(encoder, 4)

    # Building the decoder
    decoder = tflearn.fully_connected(encoder, 8)
    decoder = tflearn.fully_connected(decoder, original_dim, activation='sigmoid')

    # Regression, with mean square error
    net = tflearn.regression(decoder, optimizer='adam', learning_rate=0.001,
                             loss='mean_square', metric=None)

    # Training the auto encoder
    training_model = tflearn.DNN(net, tensorboard_verbose=0)
    training_model.fit(X, X, n_epoch=100, validation_set=(testX, testX),
              run_id="auto_encoder", batch_size=256)

    """
    # hidden_dim = 8  # original_dim//2
    # latent_dim = 4
    # original_dim = 784  # MNIST images are 28x28 pixels
    hidden_dim = 256
    latent_dim = 2
    input_x = tflearn.input_data(shape=(None, original_dim), name='input_x')
    mu, logvar = encode(input_x, hidden_dim, latent_dim)
    z = sample(mu, logvar)
    x_hat = decode(z, hidden_dim, original_dim)

    regularization_loss = calculate_regularization_loss(mu, logvar)
    reconstruction_loss = calculate_reconstruction_loss(x_hat, input_x)
    target = tf.reduce_mean(tf.add(regularization_loss, reconstruction_loss))

    net = tflearn.regression(x_hat, optimizer='rmsprop', learning_rate=0.001,
                             loss=target, metric=None, name='target_out')

    # We will need 2 models, one for training that will learn the latent
    # representation, and one that can take random normal noise as input and
    # use the decoder part of the network to generate an image

    # Train the VAE
    training_model = tflearn.DNN(net, tensorboard_verbose=0)

    model_file = "model.tflearn"
    if os.path.exists(model_file + ".meta"):
        print("Load a model from local!!!")
        training_model.load(model_file)
    else:
        # pass
        training_model.fit({'input_x': trainX}, {'target_out': trainX}, n_epoch=30,
                       validation_set=(testX, testX), batch_size=256, run_id="vae")

        training_model.save(model_file)
    """
    # Build an image generator (re-using the decoding layers)
    # Input data is a normal (gaussian) random distribution (with dim = latent_dim)
    # input_noise = tflearn.input_data(shape=[None, latent_dim], name='input_noise')
    # decoder = tflearn.fully_connected(input_noise, hidden_dim, activation='relu',
    #                                   scope='decoder_h', reuse=True)
    # decoder = tflearn.fully_connected(decoder, original_dim, activation='sigmoid',
    #                                   scope='decoder_out', reuse=True)
    # just for generate new data
    # generator_model = tflearn.DNN(decoder, session=training_model.session)
    """

    print("training sample predict:")
    print(training_model.predict(X[:3]))

    # pred_x_test = training_model.predict(testX)

    reconstruction_error = []
    anomaly_information, adjusted_threshold = get_anomaly(training_model, X, estimated_negative_sample_ratio)
    tp = fp = tn = fn = 0
    # blackY_indices = set(blackY_indices)
    for idx, (is_anomaly, dist) in enumerate(anomaly_information):
        if is_anomaly:
            print(files[idx], dist)
        predicted_label = 1 if is_anomaly else 0
        Ypred.append(predicted_label)
        reconstruction_error.append(dist)

    # print("blackY_indices len:{} detectd cnt:{}, true attack cnt:{}".format(len(blackY_indices), tp + fn, tp))
    # precision = float(tp) / (tp + fp)
    # hit_rate = float(tp) / (tp + fn)
    # accuracy = float(tp + tn) / (tp + tn + fp + fn)
    # print('precision = {}, hit_rate = {}, accuracy = {}'.format(precision, hit_rate, accuracy))

    # report_evaluation_metrics(Y, Ypred)
    # plot_training_history(history)
    # visualize_anomaly(X, reconstruction_error, adjusted_threshold)
    # plot_confusion_matrix(Y, Ypred)


def get_anomaly(model, data, estimated_negative_sample_ratio):
    target_data = model.predict(data)
    scores = np.linalg.norm(data - target_data, axis=-1)
    scores2 = np.array(scores)
    """
    np.linalg.norm(np.array([[1,1,1],[2,2,2]])-np.array([[0,0,0],[0,0,0]]),axis=-1)
    array([1.73205081, 3.46410162])
    >>> 3.46*3.46
    11.9716
    """
    scores.sort()
    cut_point = int(estimated_negative_sample_ratio * len(scores))
    threshold = scores[cut_point]
    print('estimated threshold is ' + str(threshold))
    return zip(scores2 >= threshold, scores2), threshold


if __name__ == '__main__':
    main()

然后出了一大堆误报，蛋疼！！！

estimated threshold is 15.532261382449361
('tls-SSL-HTTPS-Network-Infrastructure-10.2.211.75-61.174.11.239-6df25bceb243184a00000000.png', '15.589723319043824')
('tls-SSL-HTTPS-Network-Infrastructure-10.128.200.15-8.253.246.123-49d05bce2072185500000000.png', '15.556322765856306')
('tls-SSL-HTTPS-Network-Infrastructure-10.2.6.172-112.120.33.141-2ed75bcec42b187a00000000.png', '15.544285847781069')
('tls-SSL-HTTPS-Network-Infrastructure-10.0.96.216-124.127.247.234-d2505bcebc00187400000000.png', '15.536370031106207')
('tls-SSL-HTTPS-Network-Infrastructure-10.128.4.53-123.59.148.55-2f405bce0fcf180100000000.png', '15.545930457909789')
('tls-SSL-HTTPS-Network-Infrastructure-10.2.5.105-124.202.189.145-7cea5bceb99f231a00000000.png', '15.542118064275328')
('tls-SSL-HTTPS-Network-Infrastructure-10.2.5.105-124.202.189.104-c4615bce7b30181400000000.png', '15.643245500742289')
('tls-SSL-HTTPS-Network-Infrastructure-10.2.84.163-58.205.212.208-fc635bce84dc237100000000.png', '15.53807329897178')
('tls-SSL-HTTPS-Network-Infrastructure-10.2.69.67-88.208.61.141-88ba5bce082c187400000000.png', '15.578754079909734')

难道发现恶意的ssl流很难？？？换成CNN auto encoder试试后，直接将1024字节的ssl流看成32*32的图像进行处理：

on_server = False

if on_server:
    import matplotlib
    matplotlib.use('Agg')

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras.models import load_model
import matplotlib.pyplot as plt

from keras import backend as K
import os
from PIL import Image
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.ensemble import IsolationForest


def get_images():
    image_list = []
    files = []
    cnt = 0
    img_dir = "png2"
    for file in os.listdir(img_dir):
        path = os.path.join(img_dir, file)
        if not os.path.isfile(path):
            print("{} is not a file!!!".format(path))
            continue
        cnt += 1
        temp_image = Image.open(path).convert('L')
        # temp_image = temp_image.resize((32, 32), Image.ANTIALIAS)
        temp_image = np.asarray(temp_image) / 255.0
        image_list.append(temp_image)
        files.append(file)
    image_list = np.asarray(image_list)
    input_image = image_list.reshape([cnt, 32, 32, 1])
    return input_image, np.array(files)


def get_cnn_model():
    model = Sequential()
    # 1st convolution layer
    model.add(Conv2D(16, (3, 3)  # 16 is number of filters and (3, 3) is the size of the filter.
                     , padding='same', input_shape=(32, 32, 1)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    # 2nd convolution layer
    model.add(Conv2D(2, (3, 3), padding='same'))  # apply 2 filters sized of (3x3)
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    # -------------------------
    # 3rd convolution layer
    model.add(Conv2D(2, (3, 3), padding='same'))  # apply 2 filters sized of (3x3)
    model.add(Activation('relu'))
    model.add(UpSampling2D((2, 2)))
    # 4rd convolution layer
    model.add(Conv2D(16, (3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(UpSampling2D((2, 2)))
    # -------------------------
    model.add(Conv2D(1, (3, 3), padding='same'))
    model.add(Activation('sigmoid'))
    print(model.summary())
    model.compile(optimizer='adadelta', loss='binary_crossentropy')
    return model


data_file = "data.npz"
if os.path.exists(data_file):
    print("load data file data.npz!!!")
    data = np.load(data_file)
    X, files = data['X'], data['files']
else:
    X, files = get_images()
    np.savez(data_file, X=X, files=files)


x_train, x_test, y_train, y_test = train_test_split(X, X, test_size=0.05, random_state=42)

model_file = 'model.h5'
if os.path.exists(model_file):
    print("found model, load it from disk!!!")
    model = load_model('model.h5')
else:
    model = get_cnn_model()

# resume training
model.fit(x_train, x_train, epochs=30, batch_size=1024, validation_data=(x_test, x_test))
model.save(model_file)

restored_imgs = model.predict(x_test)
print("just see some test:")
for i in range(5):
    print(x_test[i])
    plt.imshow(x_test[i].reshape(32, 32))
    plt.gray()
    if on_server:
        plt.savefig("test-{}.png".format(i))
    else:
        plt.show()

    print(x_test[i])
    print(restored_imgs[i])
    plt.imshow(restored_imgs[i].reshape(32, 32))
    plt.gray()
    if on_server:
        plt.savefig("test-{}-restored.png".format(i))
    else:
        plt.show()

    print("----------------------------")

layers = len(model.layers)

for i in range(layers):
    print(i, ". ", model.layers[i].output.get_shape())

"""
0 .  (?, 28, 28, 16)
1 .  (?, 28, 28, 16)
2 .  (?, 14, 14, 16)
3 .  (?, 14, 14, 2)
4 .  (?, 14, 14, 2)
5 .  (?, 7, 7, 2)
6 .  (?, 7, 7, 2)
7 .  (?, 7, 7, 2)
8 .  (?, 14, 14, 2)
9 .  (?, 14, 14, 16)
10 .  (?, 14, 14, 16)
11 .  (?, 28, 28, 16)
12 .  (?, 28, 28, 1)
13 .  (?, 28, 28, 1)
"""
"""
(0, '. ', TensorShape([Dimension(None), Dimension(28), Dimension(28), Dimension(1)]))
(1, '. ', TensorShape([Dimension(None), Dimension(28), Dimension(28), Dimension(16)]))
(2, '. ', TensorShape([Dimension(None), Dimension(14), Dimension(14), Dimension(16)]))
(3, '. ', TensorShape([Dimension(None), Dimension(14), Dimension(14), Dimension(8)]))
(4, '. ', TensorShape([Dimension(None), Dimension(7), Dimension(7), Dimension(8)]))
(5, '. ', TensorShape([Dimension(None), Dimension(7), Dimension(7), Dimension(8)]))
(6, '. ', TensorShape([Dimension(None), Dimension(4), Dimension(4), Dimension(8)]))
(7, '. ', TensorShape([Dimension(None), Dimension(4), Dimension(4), Dimension(8)]))
(8, '. ', TensorShape([Dimension(None), Dimension(8), Dimension(8), Dimension(8)]))
(9, '. ', TensorShape([Dimension(None), Dimension(8), Dimension(8), Dimension(8)]))
(10, '. ', TensorShape([Dimension(None), Dimension(16), Dimension(16), Dimension(8)]))
(11, '. ', TensorShape([Dimension(None), Dimension(14), Dimension(14), Dimension(16)]))
(12, '. ', TensorShape([Dimension(None), Dimension(28), Dimension(28), Dimension(16)]))
(13, '. ', TensorShape([Dimension(None), Dimension(28), Dimension(28), Dimension(1)]))
"""

#layer[7] is activation_3 (Activation), it is compressed representation
get_3rd_layer_output = K.function([model.layers[0].input], [model.layers[7].output])
"""
# compressed = get_3rd_layer_output([x_test])[0]
compressed = get_3rd_layer_output([X])[0]
print(compressed[:3])
#layer[7] is size of (None, 7, 7, 2). this means 2 different 7x7 sized matrixes. We will flatten these matrixes.
compressed = compressed.reshape(len(X), 8*8*2)
print("some sample data compressed:")
print(compressed[:3])
"""

chunks = []
N = 3000
for i in range(0, len(X), N):
    chunk_data = X[i:i+N]
    print("chunk data length:", len(chunk_data))
    compressed = get_3rd_layer_output([chunk_data])[0]
    chunk_compressed = compressed.reshape(len(chunk_data), 8 * 8 * 2)
    # print("len of compressed:", len(chunk_compressed))
    chunks.append(chunk_compressed)
compressed = np.concatenate(chunks)
assert len(compressed) == len(files)

print("some sample data compressed:")
print(compressed[:3])


rng = np.random.RandomState(42)
# clf = IsolationForest(max_samples=10*2, random_state=rng)
# clf = IsolationForest(max_features=5)
clf = IsolationForest(max_samples="auto", random_state=rng, contamination=0.0001)
clf.fit(compressed)
pred_y = clf.predict(compressed)

cnt = 0
for i, y in enumerate(pred_y):
    if y == -1:
        print("bad data:", files[i])
        cnt += 1
        plt.imshow(X[i].reshape(32, 32))
        plt.gray()
        if on_server:
            plt.savefig("anom-{}.png".format(files[i]))
        else:
            plt.show()

print("cnt:{}".format(cnt))

 然后检测的结果：

anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.141.22-140.143.254.151-7a945bce6580183800000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.152.184-139.198.13.247-9b575bce61aa183900000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.152.229-54.243.242.217-5d035bce7ae2180100000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.153.170-58.205.220.35-90945bce62db237100000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.153.84-120.132.53.247-56955bce9e60181700000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.156.96-120.27.81.165-d1015bcea15c183400000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.158.185-111.30.138.183-18645bcea2de182f00000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.164.168-175.102.18.142-d42a5bce5eda180400000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.169.126-117.78.58.102-06b15bce6c0b182200000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.204.20-59.37.96.226-394a5bceafcd234800000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.210.113-207.148.117.221-5cac5bce7b51234600000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.210.126-151.101.76.223-eeb55bce6578233900000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.210.50-47.107.215.152-192d5bce7f3d237600000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.211.177-128.199.185.96-c0425bce77aa232900000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.230.241-180.153.222.195-301b5bce96aa185900000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.2.33-47.92.124.196-2cba5bd1b021185900000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.35.34-59.110.185.99-43975bcea358234100000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.40.147-203.100.92.177-ef7a5bce82f2181300000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.42.152-23.198.101.111-ddce5bce9021185200000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.42.216-67.216.207.162-19fc5bce712c184000000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.47.101-54.222.139.132-87465bceab54232b00000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.47.157-120.55.104.178-c6f25bce6358232100000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.48.226-59.37.96.226-0a5c5bce7a7a182c00000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.48.57-47.100.42.159-19995bce807b232e00000000.png.png
anom-tls-SSL-HTTPS-Network-Infrastructure-10.0.53.122-115.27.243.5-5bcb5bce8151183b00000000.png.png

 没有查到几个是恶意的。。。真是有种想吐血的感觉！！！

接下来尝试下GAN进行异常检测，但是换一个思路了，不再是完全无监督思路，而是先过滤出异常的ssl，然后使用GAN来检测类似的异常。