Impact of network attacks implementation on performance metrics of simulated mobile adhoc network segment

Abstract

Mobile adhoc networks are one of the promising areas of the edge computing paradigm and used in a wide variety of areas included but not limited to intelligent transport systems, smart homes and smart cities and so on. The main feature of mobile adhoc networks is the constantly changing dynamic network topology, as a result of which it is necessary to use reactive routing protocols when transferring packets between nodes in the network. Mobile adhoc networks are vulnerable to cyber-attacks of various kinds, so there is a need to develop measures to identify such network threats and develop rules for responding to emerging network security incidents. This paper presents a model for detecting traffic anomalies in wireless distributed adhoc networks based on machine learning methods, as well as an experimental study of the simulation of a network segment in terms of performance degradation for the case of various scenarios of network attacks implementation. Distributed denial-of-service attack and cooperative blackhole attack have the most impact on performance metrics degradation in mobile adhoc networks.