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 susceptible to different 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.

翻译：移动自组网（MANET）是一种分布式、去中心化的无线便携节点网络，节点间无需固定通信基站或集中式管理即可直接连接。MANET中的节点以随机方向持续移动并遵循任意方式，这给网络带来了诸多挑战，使其更易遭受不同的安全威胁。由于其整体架构的去中心化特性，加之硬件资源的局限性，这些无基础设施网络更易受到黑洞攻击、网络分割、节点自私性及拒绝服务（DoS）攻击等不同安全威胁。本研究旨在提出、探讨并设计一种利用深度学习人工神经网络（ANN）的移动自组网入侵检测预测技术。本文通过基于仿真的评估和深度ANN建模，用于检测并隔离拒绝服务（DoS）攻击，以提升移动自组网的整体安全水平。