malware-detection-with-deep-learning-autoencoder/scan.py at master · slrbl/malware-detection-with-deep-learning-autoencoder · GitHub

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from utilities import *
from keras.models import load_model
import numpy as np

autoencoder_model = load_model('./MODEL/autoencoder_model.h5')

#binary_data = pd.read_csv("./SAMPLES/wannacry_data/wannaCry.csv")
binary_data = pd.read_csv("./SAMPLES/benign_binary.csv")

print(binary_data)
binary_data = harmonize(binary_data)

print(binary_data)

reconstructed_binary_data = autoencoder_model.predict(binary_data)

binary_reconstruction_mse = np.mean(np.power(binary_data - reconstructed_binary_data, 2), axis=1)

with open("./MODEL/model_data.json", "r") as f:
    data = json.load(f)

error=binary_reconstruction_mse.iloc[0]
threshold = data["threshold"]
confidence = 100-(1 - abs(error - threshold) / threshold) * 100

confidence=99 if confidence>100 else confidence

print("Threshold: {}".format(threshold))
print("Binary reconstruction error: {}".format(error))
if error<=threshold:
    print('Verdict: Malware')
else:
    print('Verdict: Benign')

print("Confidence:{} %".format(confidence))