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:移动自动网(MANET)是一个分布式分散的无线便携式节点网络,没有固定通信基站或中央行政管理,直接连接无固定通信基站或中央管理,无线便携式节点无线网络是分散的、分散的网络。该网的节点不断随机地移动,向这些网络提出了许多挑战,使其更容易受到不同的安全威胁。由于其总体结构的这种分散性质,加上硬件资源有限,这些无基础设施的网络更容易受到不同的安全攻击,如黑洞攻击、网络隔断、节点自私和拒绝服务攻击。这项工作的目的是利用深思熟虑的人工神经网络为移动特设网络提供、调查和设计入侵探测预测技术。模拟评估和深度非驻地网络模型用于探测和隔离拒绝服务攻击,以提高移动临时网络的总体安全水平。</s>