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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # In[2]: | |
| import gradio as gr | |
| import os | |
| import pandas as pd | |
| from math import sqrt; | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| #load packages for ANN | |
| import tensorflow as tf | |
| def malware_detection_DL (results, malicious_traffic, benign_traffic): | |
| plt.clf() | |
| if os.path.exists("accplot.png"): | |
| os.remove("accplot.png") | |
| else: | |
| pass | |
| if os.path.exists("lossplot.png"): | |
| os.remove("lossplot.png") | |
| else: | |
| pass | |
| 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(reduced_x, reduced_y,validation_split=0.33, batch_size=32, epochs = 10,verbose=0) | |
| _, accuracy = model.evaluate(reduced_x, reduced_y) | |
| #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') | |
| plt.savefig('accplot.png') | |
| return "accplot.png",accuracy | |
| 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') | |
| plt.savefig('lossplot.png') | |
| return 'lossplot.png',accuracy | |
| 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") | |
| ],["image","text"], theme="grass" | |
| ) | |
| iface.launch(enable_queue = True) |