![Python program to build deep learning algorithm using a CNNs model to detect malware attacks.
The code is working, but I faced issues with the last part of the code, and I needed to know
precisely where the problem was.
I have the dataset of malware and benign in exe, I convert them to grayscale images, and it goes
through the filters, and it shows me the output.
this is the code
import keras from keras.models import Sequential from keras.layers import Dense, Dropout,
Activation, Flatten from keras.layers import Conv2D, MaxPooling2D import numpy as np from
os import listdir from PIL import Image import os.path import numpy as np path =
'/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE' h = 256
#height of image w = 256 #width of image # be careful with using this function, it will consume
memory, access to disk and time images = [] for f in listdir('/media/malkuwari/New
Volume/Ransomware_Detection_using _CNN/mixEXE'): with
open(os.path.join('/media/malkuwari/New Volume/Ransomware_Detection_using
_CNN/mixEXE', f), 'rb') as img_set: img_arr = img_set.read(h*w) while img_arr: if len(img_arr)
== h*w and img_arr not in images: images.append(img_arr) img_arr = img_set.read(h*w) #And
you can save them into png files # count = 0 # for img in images: # png =
Image.fromarray(np.reshape(list(img), (h,w)).astype('float32'), mode='L') #
png.save('image_l%d.png'%count) # count += 1 #reshape images to fit into the CNN model
img_list = np.zeros(shape = (len(images), h,w,1), dtype = np.uint8) for j in range(len(images)):
img_list[j,:,:,0] = np.reshape(list(images[j]), (h,w)) img_list = img_list.astype('float32') img_list
/= 255 # ################################################################
#approca 1 failed go back from scrtuch # implement the architecture of the CNN model =
Sequential() #Conv2D Layers model.add(Conv2D(12, (25, 25),
padding='same',input_shape=img_list.shape[1:], activation = 'relu')) model.add(Conv2D(12, (25,
25), activation = 'relu')) #Max Pooling Layer model.add(MaxPooling2D(pool_size=(2, 2)))
#Conv2D Layer model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu'))
model.add(Conv2D(12, (13, 13), activation = 'relu')) #Max Pooling
model.add(MaxPooling2D(pool_size=(2, 2))) #Flattening Layer model.add(Flatten()) #Dense
Layer model.add(Dense(1024, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy'])
model.summary()
''' FINALLY If you have a set of executable files from both types (benign and malicious) you can
try to fit the model. You should prepare a list of images for malwares and a list for benign and
here how you fit the model. ''' #Change the parameters to whatever suits you
I already have to files that contain benign files and malicious images, but it shows me
syntax error!](https://image.slidesharecdn.com/pythonprogramtobuilddeeplearningalgorithmusingacnnsmodelto-230222052326-0ac14ed3/85/Python-program-to-build-deep-learning-algorithm-using-a-CNNs-model-to-docx-1-320.jpg)
![batch_size = 512 epochs = 100 labels = [0 for _ in benign_images ] + [1 for _ in
malicious_images ] model.fit( benign_images + malicious_images , labels, batch_size =
batch_size, epochs = epochs, validation_split = 0.25,
please, if you can help me with this issue and if there is additional coding needed to my project,
thank you](https://image.slidesharecdn.com/pythonprogramtobuilddeeplearningalgorithmusingacnnsmodelto-230222052326-0ac14ed3/85/Python-program-to-build-deep-learning-algorithm-using-a-CNNs-model-to-docx-2-320.jpg)
Python program to build deep learning algorithm using a CNNs model to.docx
- 1. Python program to build deep learning algorithm using a CNNs model to detect malware attacks. The code is working, but I faced issues with the last part of the code, and I needed to know precisely where the problem was. I have the dataset of malware and benign in exe, I convert them to grayscale images, and it goes through the filters, and it shows me the output. this is the code import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D import numpy as np from os import listdir from PIL import Image import os.path import numpy as np path = '/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE' h = 256 #height of image w = 256 #width of image # be careful with using this function, it will consume memory, access to disk and time images = [] for f in listdir('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE'): with open(os.path.join('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE', f), 'rb') as img_set: img_arr = img_set.read(h*w) while img_arr: if len(img_arr) == h*w and img_arr not in images: images.append(img_arr) img_arr = img_set.read(h*w) #And you can save them into png files # count = 0 # for img in images: # png = Image.fromarray(np.reshape(list(img), (h,w)).astype('float32'), mode='L') # png.save('image_l%d.png'%count) # count += 1 #reshape images to fit into the CNN model img_list = np.zeros(shape = (len(images), h,w,1), dtype = np.uint8) for j in range(len(images)): img_list[j,:,:,0] = np.reshape(list(images[j]), (h,w)) img_list = img_list.astype('float32') img_list /= 255 # ################################################################ #approca 1 failed go back from scrtuch # implement the architecture of the CNN model = Sequential() #Conv2D Layers model.add(Conv2D(12, (25, 25), padding='same',input_shape=img_list.shape[1:], activation = 'relu')) model.add(Conv2D(12, (25, 25), activation = 'relu')) #Max Pooling Layer model.add(MaxPooling2D(pool_size=(2, 2))) #Conv2D Layer model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu')) model.add(Conv2D(12, (13, 13), activation = 'relu')) #Max Pooling model.add(MaxPooling2D(pool_size=(2, 2))) #Flattening Layer model.add(Flatten()) #Dense Layer model.add(Dense(1024, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy']) model.summary() ''' FINALLY If you have a set of executable files from both types (benign and malicious) you can try to fit the model. You should prepare a list of images for malwares and a list for benign and here how you fit the model. ''' #Change the parameters to whatever suits you I already have to files that contain benign files and malicious images, but it shows me syntax error!
- 2. batch_size = 512 epochs = 100 labels = [0 for _ in benign_images ] + [1 for _ in malicious_images ] model.fit( benign_images + malicious_images , labels, batch_size = batch_size, epochs = epochs, validation_split = 0.25, please, if you can help me with this issue and if there is additional coding needed to my project, thank you