AIRCC PUBLISHING CORPORATION
AN INTRUSION DETECTION MECHANISM FOR MANETS BASED ON DEEP LEARNING ARTIFICIAL NEURAL NETWORKS (ANNS)
Mohamad T Sultan1,2, Hesham El Sayed1,2 and Manzoor Ahmed Khan3
1College of Information Technology United Arab Emirates University, Abu Dhabi, UAE 2Emirates Center for Mobility Research (ECMR), United Arab Emirates University, United Arab Emirates 3College of Information Technology United Arab Emirates University, Abu Dhabi, UAE
Mobile Ad-hoc Network (MANET) is a distributed, decentralized network of wireless portable nodes connecting directly without any fixed communication base station or centralized administration. Nodes in MANET move continuously in random directions and follow an arbitrary manner, which presents numerous challenges to these networks and make them more susceptibletodifferent security threats. Due to this decentralized nature of their overall architecture, combined with the limitation of hardware resources, those infrastructure-less networks are more susceptible to different security attacks such as black hole attack, network partition, node selfishness, and Denial of Service (DoS) attacks. This work aims to present, investigate, and design an intrusion detection predictive technique for Mobile Ad hoc networks using deep learning artificial neural networks (ANNs). A simulation-based evaluation and a deep ANNs modelling for detecting and isolating a Denial of Service (DoS) attack are presented to improve the overall security level of Mobile ad hoc networks.
Figure 1. Example of a Petri Net