Abstract

Herd immunity, one of the most fundamental concepts in network epidemics, occurs when a large fraction of the population of devices is immune against a virus or malware. The few individuals who have not taken countermeasures against the threat are assumed to have very low chances of infection, as they are indirectly protected by the rest of the devices in the network. Although very fundamental, herd immunity does not account for strategic attackers scanning the network for vulnerable nodes. In face of such attackers, nodes who linger vulnerable in the network become easy targets, compromising cybersecurity. In this paper, we propose an analytical model which allows us to capture the impact of countermeasures against attackers when both in-network as well as exogenous infections coexist. Using the proposed model, we show that a diverse set of potential attacks produces non-trivial equilibria, some of which go counter to herd immunity; e.g., our model suggests that nodes should adopt countermeasures even when the remainder of the nodes has already decided to do so. INDEX TERMS Cybersecurity, denial-of-service attacks, network epidemics, network security

Highlights

  • Malicious software, such as viruses, Internet worms, adware, spyware and botnets [1], continuously threatens the Internet stability posing a wide variety of challenges to system administrators and users

  • 2) EXPERIMENTAL RESULTS Figure 10 compares the infection probability obtained through simulations against that obtained with the proposed analytical model

  • In this paper we have proposed a new epidemic analytical model to assess the infection probability of nodes in a network which face a strategic attacker with finite power

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Summary

Introduction

Malicious software, such as viruses, Internet worms, adware, spyware and botnets [1], continuously threatens the Internet stability posing a wide variety of challenges to system administrators and users. Viral models for the diffusion of malicious software have been part of the mainstream research in network security to model the diffusion of computer worms [2]–[6]. Such models are very convenient to capture the construction of large distributed attack networks known as botnets [7], [8], which are pivotal for the emerging paradigm. Botnets have been leased as support infrastructure in order to perform various types of criminal activities including, e.g., Distributed Denial of Service (DDoS) [9] attack campaigns or ramsomware attacks, just to mention the most spectacular ones.

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