Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case
Aurelle Tchagna Kouanou, Théophile Fonzin Fozin, Franck Mani Zanga, Adèle Ngo Mouelas, Gerad Nzebop Ndenoka, Michael Sone Ekonde
Abstract
This paper addresses the security concerns associated with Mobile Ad-hoc Networks (MANET) and proposes a new method for detecting and preventing attacks using machine learning. The study involved the creation of a MANET with 26 nodes in NetSim (Network Simulator) software, followed by the implementation of wormhole and blackhole attacks. A dataset was generated from the network traffic obtained during the simulations, and a machine-learning model was designed to predict and detect these attacks. The model achieved high sensitivity, accuracy and f1 scores of 99%. The effectiveness of the model was tested by developing a real-time application. This method can be applied to any wireless network and is particularly relevant for companies that use Ad-hoc networks for communication.