Biblio

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.

Current procedures for anomaly detection in self-organizing mobile communication networks use network-centric approaches to identify dysfunctional serving nodes. In this paper, a user-centric approach and a novel methodology for anomaly detection is proposed, where the Quality of Experience (QoE) metric is used to evaluate the end-user experience. The system model demonstrates how dysfunctional serving eNodeBs are successfully detected by implementing a parametric QoE model using machine learning for prediction of user QoE in a network scenario created by the ns-3 network simulator. This approach can play a vital role in the future ultra-dense and green mobile communication networks that are expected to be both self- organizing and self-healing.