Title | A Study and Simulation Research of Blackhole Attack on Mobile AdHoc Network |
Publication Type | Conference Paper |
Year of Publication | 2018 |
Authors | Li, Guoquan, Yan, Zheng, Fu, Yulong |
Conference Name | 2018 IEEE Conference on Communications and Network Security (CNS) |
Keywords | AODV, AODV protocol, Blackhole attack, blackhole attack feature extraction, blackhole nodes, end-to-end delay, external attacks, feature extraction, intermediate nodes, internal attacks, Internet-of-Things, IoT, Loss measurement, MANET, MANET security measurement, mobile ad hoc network, mobile ad hoc networks, mobile multihop network, mobile nodes, network performance parameters, Network security, ns-3, ns-3 network simulator, Packet loss rate, pubcrawl, Resiliency, Routing, Routing protocols, Scalability, security, telecommunication security, Throughput |
Abstract | Mobile ad hoc network (MANET) is a kind of mobile multi-hop network which can transmit data through intermediate nodes, it has been widely used and become important since the growing of the market of Internet of Things (IoT). However, the transmissions on MANET are vulnerable, it usually suffered with many internal or external attacks, and the research on security topics of MANET are becoming more and more hot recently. Blackhole Attack is one of the most famous attacks to MANET. In this paper, we focus on the Blackhole Attack in AODV protocol, and use NS-3 network simulator to study the impact of Blackhole Attack on network performance parameters, such as the Throughput, End-to-End Delay and Packet Loss Rate. We further analyze the changes in network performance by adjusting the number of blackhole nodes and total nodes, and the movement speed of mobile nodes. The experimental results not only reflect the behaviors of the Blackhole Attack and its damage to the network, but also provide the characteristics of Blackhole Attacks clearly. This is helpful to the research of Blackhole Attack feature extraction and MANET security measurement. |
DOI | 10.1109/CNS.2018.8433148 |
Citation Key | li_study_2018 |