Supervised Machine Learning to Enhance Security in Mobile Ad Hoc Networks
Marwa Mohammed Khalifa, Osman Nuri UCAN, Khattab M. Ali Alheeti · 2021 14th International Conference on Developments in eSystems Engineering (DeSE) · 2021
Mobile Ad hoc Networks (MANET) provide a service in one way or another through network applications in several diverse areas due to the multiple characteristics of MANET, such as high-level mobility, decentralization of nodes, and physical insecurity. The Intrusion Detection System (IDS) function detects threats and stops them before they disrupt the network. Supervised machine learning techniques are easy to implement and comprehend because they use the knowledge and experience gathered from the available data to classify network nodes into normal or malicious. To enhance security in MANET networks in this paper, we proposed a security detection system employing classify misbehavior of nodes depending on supervised machine learning techniques such as Random Forest (RF) and Naive Bayes(NB) techniques. Using a network simulator-2 (ns-2) simulation was made to produce a data set, explained Our experimental results for our proposed system have good accuracy for detecting intrusion in the system. The RF was more efficient.