I have tried running this code below, and it is working, but the accuracy is less than expected.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_loss value
not decreased
earlystop = EarlyStopping(patience=10)
# Learning Rate Reduction
learning_rate_reduction = ReduceLROnPlateau(mon.
Z Score,T Score, Percential Rank and Box Plot Graph
I have tried running this code below- and it is working- but the accur.pdf
1. I have tried running this code below, and it is working, but the accuracy is less than expected.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
2. from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:RansomSecondApproachRansomware_Detection_using
_CNNMixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:RansomSecondApproachRansomware_Detection_using
_CNNMixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
3. 'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
4. model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_loss value
not decreased
earlystop = EarlyStopping(patience=10)
# Learning Rate Reduction
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=2,
verbose=1,
factor=0.5,
min_lr=0.00001)
callbacks = [earlystop, learning_rate_reduction]
#Prepare data
df["category"] = df["category"].replace({0: 'benign', 1: 'malware'})
train_df, validate_df = train_test_split(df, test_size=0.20, random_state=42)
train_df = train_df.reset_index(drop=True)
validate_df = validate_df.reset_index(drop=True)
total_train = train_df.shape[0]
total_validate = validate_df.shape[0]
batch_size= 15 # defualt 15
# Traning Generator
# Defualt
![from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:RansomSecondApproachRansomware_Detection_using
_CNNMixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:RansomSecondApproachRansomware_Detection_using
_CNNMixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)