Dynamics of Botnet Propagation in Software Defined Networks Using Epidemic Models

Abstract

During COVID-19 the new normal became an increased reliance on remote connectivity, and that fact is far away to change any time soon. The increasing number of networked devices connected to the Internet is causing an exponential growth of botnets. Subsequently, the number of DDoS (Distributed Denial of Service) attacks registered around the world also increased, especially during the pandemic lockdown. Therefore, it is crucial to understand how botnets are formed and how bots propagate within networks. In particular, analytic modelling of the botnets epidemic process is an essential component for understanding DDoS attacks, and thus mitigate their impact. In this paper, we propose two analytic epidemic models; (i) the first one for enterprise Software Define Networks (SDN) based on the SEIRS (Susceptible -Exposed -Infected -Recovered) approach, while (ii) the second model is designed for service providers' SDN, and it is based on a novel extension of a SEIRS-SEIRS vector-borne approach. Both models illustrate how bots spread in different types of SDN networks. We found that bot infection behaves in a similar way to human epidemics, such as the novel COVID-19 outbreak. We present the calculation of the basic reproduction number R o for both models and we test the system stability using the next generation matrix approach. We have validated the models using the final value theorem (FVT), with which we can determine the steadystate values that provide a better understanding of the propagation process.

Resilience enhancement strategy for cyber–physical distribution systems that considers cross‐space propagation of information risk

“…In the research of virus propagation in communication networks and computer networks, epidemic models are often used for quantitative risk analysis [25]. Referring to this method, this paper introduces an improved epidemic model which called the SIOR model, to analyze the lateral diffusion of information risk in the CPDS communication network.…”

VIQS Epidemic Modeling of Distributed Denial-of-Service Attacks in Software-Defined Networking

“…Many approaches have been applied to mitigate DDoS attacks including entropy mechanism ( [8], [9], [10] and [11]), blockchain method ( [12], [13], [14] and [15]), machine learning approach ( [16], [17], [18] and [19]), statistical technique ( [20], [21] and [22]) and epidemic approach [23], [24], [25], [26], [27], [28], [29] and [30]). Though, each of these approaches has contributed to some extents to the mitigation of DDoS attacks in networks.…”