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

Abstract

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.

TAARA Method for Processing on the Network Forensics in the Event of an ARP Spoofing Attack

“…Other studies use the forensic process model when performing network forensics on MITM attacks, which are part of advanced network attacks [14]. MITM attacks are frequently linked to credential theft, which is considered a cybercrime, and a method for sniffing the victim's network traffic communications against the gateway that are exchanged between users in some systems covertly [15], [16] . Among the cybercrime attacks mentioned, the effect of the ARP spoofing attack initiator is one example [17][18][19].…”