Spaces:
Runtime error
Runtime error
File size: 3,797 Bytes
b3a8c59 |
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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
import gradio as gr
import pandas as pd
from math import sqrt;
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression;
from sklearn.metrics import accuracy_score, r2_score, confusion_matrix, mean_absolute_error, mean_squared_error, f1_score, log_loss
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import joblib
#load packages for ANN
import tensorflow as tf
def malware_detection_DL (results, malicious_traffic, benign_traffic):
malicious_dataset = pd.read_csv(malicious_traffic) #Importing Datasets
benign_dataset = pd.read_csv(benign_traffic)
# Removing duplicated rows from benign_dataset (5380 rows removed)
benign_dataset = benign_dataset[benign_dataset.duplicated(keep=False) == False]
# Combining both datasets together
all_flows = pd.concat([malicious_dataset, benign_dataset])
# Reducing the size of the dataset to reduce the amount of time taken in training models
reduced_dataset = all_flows.sample(38000)
#dataset with columns with nan values dropped
df = reduced_dataset.drop(reduced_dataset.columns[np.isnan(reduced_dataset).any()], axis=1)
#### Isolating independent and dependent variables for training dataset
reduced_y = df['isMalware']
reduced_x = df.drop(['isMalware'], axis=1);
# Splitting datasets into training and test data
x_train, x_test, y_train, y_test = train_test_split(reduced_x, reduced_y, test_size=0.2, random_state=42)
#scale data between 0 and 1
min_max_scaler = preprocessing.MinMaxScaler()
x_scale = min_max_scaler.fit_transform(reduced_x)
# Splitting datasets into training and test data
x_train, x_test, y_train, y_test = train_test_split(x_scale, reduced_y, test_size=0.2, random_state=42)
#type of layers in ann model is sequential, dense and uses relu activation
ann = tf.keras.models.Sequential()
model = tf.keras.Sequential([
tf.keras.layers.Dense(32, activation ='relu', input_shape=(373,)),
tf.keras.layers.Dense(32, activation = 'relu'),
tf.keras.layers.Dense(1, activation = 'sigmoid'),
])
model.compile(optimizer ='adam',
loss = 'binary_crossentropy',
metrics = ['accuracy'])
#model.fit(x_train, y_train, batch_size=32, epochs = 150, validation_data=(x_test, y_test))
#does not output epochs and gives evalutaion of validation data and history of losses and accuracy
history = model.fit(x_train, y_train, batch_size=32, epochs = 150,verbose=0, validation_data=(x_test, y_test))
_, accuracy = model.evaluate(x_train, y_train)
#return history.history
if results=="Accuracy":
#summarize history for accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
return plt.show()
else:
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
return plt.show()
iface = gr.Interface(
malware_detection_DL, [gr.inputs.Dropdown(["Accuracy","Loss"], label="Result Type"),
gr.inputs.Dropdown(["malicious_flows.csv"], label = "Malicious traffic in .csv"), gr.inputs.Dropdown(["sample_benign_flows.csv"], label="Benign Traffic in .csv")
], "plot",
)
iface.launch()
|