create app.py file

app.py

@@ -0,0 +1,87 @@
+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()
+