DynamicMalwareAnalysisUsingDNN/Malware_Detection_Using_DeepLearning (1).py at master · ShantanuKT/DynamicMalwareAnalysisUsingDNN · GitHub

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#!/usr/bin/env python
# coding: utf-8

# In[56]:


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report

malData=pd.read_csv("MalwareData.csv",sep="|")
malData=malData.sample(frac=1).reset_index(drop=True)
dataset=malData.drop(["legitimate","Name","md5"],axis=1)

legitimate_col=malData["legitimate"]


# In[15]:


X_train=dataset[0:124242].to_numpy()
Y_train=legitimate_col[0:124242].to_numpy()


# In[16]:


X_test=dataset[124242::].to_numpy()
Y_test=legitimate_col[124242::].to_numpy()


# In[4]:


from keras.models import Sequential
from keras.layers import Dense


# In[49]:


model=Sequential()
model.add(Dense(100,input_dim=54,activation='relu'))
model.add(Dense(50,activation='relu'))
model.add(Dense(25,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
print(model.summary())


# In[50]:


model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])


# In[51]:


history=model.fit(X_train,Y_train,epochs=30,batch_size=10000)


# In[52]:


losses=history.history['loss']

epochs=range(1,len(losses)+1)
plt.plot(epochs,losses)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.show()


# In[53]:


accuracy=history.history['accuracy']
plt.plot(epochs,accuracy)
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.show()


# In[64]:


pred=model.predict(X_test)
pred_int=pred.astype(int)
#print(pred_int)
print(classification_report(Y_test,pred_int))


# In[65]:


_,accuracy =model.evaluate(X_test,Y_test)
print(accuracy)


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