IoT-Malware-Detection-Solution-Using-CNN-Classification/Model_2.py at main · nmthuann/IoT-Malware-Detection-Solution-Using-CNN-Classification · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
from tensorflow import keras

# Alex Net (2012) + VGG (2014)
def create_model_2(input_shape):
    model = keras.models.Sequential([
        # # the first - Conv2D
        # keras.layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), activation='relu', padding='same'),
        # keras.layers.BatchNormalization(),
        # # keras.layers.MaxPool2D(pool_size=(2,2), strides=(1,1)),
        # # the second - Conv2D
        # keras.layers.Conv2D(filters=32, kernel_size=(3,3), strides=(2,2), activation='relu', padding="same"),
        # keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),
        # keras.layers.BatchNormalization(),
        #
        # # the first - Conv2D
        # keras.layers.Conv2D(filters=128, kernel_size=(3, 3), strides=(2, 2), activation='relu', padding='same'),
        # keras.layers.BatchNormalization(),
        # keras.layers.Conv2D(filters=128, kernel_size=(3, 3), strides=(2, 2), activation='relu', padding='same'),
        # keras.layers.BatchNormalization(),
        #
        # keras.layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2)),
        #
        # # the first - Conv2D
        # keras.layers.Conv2D(filters=256, kernel_size=(1, 1), strides=(2, 2), activation='relu', padding='same'),
        # keras.layers.Conv2D(filters=256, kernel_size=(3, 3), strides=(2, 2), activation='relu', padding='same'),
        # keras.layers.Conv2D(filters=256, kernel_size=(1, 1), strides=(2, 2), activation='relu', padding='same'),
        # keras.layers.BatchNormalization(),
        # keras.layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2)),
        #
        # keras.layers.Conv2D(filters=728, kernel_size=(1, 1), strides=(2, 2), activation='relu', padding='same'),
        # keras.layers.Conv2D(filters=728, kernel_size=(3, 3), strides=(2, 2), activation='relu', padding='same'),
        # keras.layers.Conv2D(filters=728, kernel_size=(1, 1), strides=(2, 2), activation='relu', padding='same'),
        # keras.layers.BatchNormalization(),
        # keras.layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2)),

        keras.layers.Conv2D(filters=64, kernel_size=(1, 1), strides=(1, 1), activation='relu', padding='same'),
        keras.layers.MaxPool2D(pool_size=(2, 2), strides=(1, 1)),

        keras.layers.Conv2D(filters=128, kernel_size=(1, 1), strides=(1, 1), activation='relu', padding='same'),
        keras.layers.MaxPool2D(pool_size=(2, 2), strides=(1, 1)),

        keras.layers.Conv2D(filters=128, kernel_size=(1, 1), strides=(1, 1), activation='relu', padding='same'),


        keras.layers.Flatten(input_shape=(64,64,8)),# 64*64*8
        keras.layers.Dense(128, activation='relu'),
        keras.layers.Dropout(0.5),
        keras.layers.Dense(2, activation='softmax')
    ])

    return model