Implementation of CNN-MLP and CNN-LSTM for MitM Attack
Detection System

Implementation of CNN-MLP and CNN-LSTM for MitM Attack
Detection System

MitM, Kitsune Network Attack Dataset, CNN-MLP, CNN-LSTM

Man in the Middle (MitM) is one of the attack techniques conducted for eavesdropping on data transitions or conversations
between users in some systems secretly. It has a sizeable impact because it could make the attackers will do another attack,
such as website or system deface or phishing. Deep Learning could be able to predict various data well. Hence, in this study,
we would like to present the approach to detect MitM attacks and process its data, by implementing hybrid deep learning
methods. We used 2 (two) combinations of the Deep Learning methods, which are CNN-MLP and CNN-LSTM. We also used
various Feature Scaling methods before building the model and will determine the better hybrid deep learning methods for
detecting MitM attack, as well as the feature selection methods that could generate the highest accuracy. Kitsune Network
Attack Dataset (ARP MitM Ettercap) is the dataset used in this study. The results prove that CNN-MLP has better results than
CNN-LSTM on average, which has the accuracy rate respectively at 99.74%, 99.67%, and 99.57%, and using Standard Scaler
has the highest accuracy (99.74%) among other scenarios

Hartina Hiromi Satyanegara1
, Kalamullah Ramli2

Universitas Indonesia

30-06-2022

Fajar bagus W

PDF

Indonesia

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