Analytical Models for Epidemics in Mobile Networks

Mickens, James; Noble, Brian

doi:10.1109/wimob.2007.4390871

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…al. were among the first to examine device-todevice spreading of malicious software in mobile ad hoc networks [19,18]. In their work, they present a queue-based technique for the RWP model to overcome the limitations of the earlier homogeneous models of Kephart and White.…”

Section: Related Workmentioning

confidence: 99%

Epidemic Spread in Mobile Ad Hoc Networks: Determining the Tipping Point

Valler

Prakash

Tong³

et al. 2011

Networking 2011

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Abstract. Short-range, point-to-point communications for mobile users enjoy increasing popularity, particularly with the rise in Bluetooth -equipped mobile devices. Unfortunately, virus writers have begun exploiting lax security in many mobile devices and subsequently developed malware exploiting proximity-based propagation mechanisms (e.g. Cabir or CommWarrior). So, if given an ad-hoc network of such mobile users, will a proximity-spreading virus survive or die out; that is, can we determine the "tipping point" between survival and die out? What effect does the average user velocity have on such spread? We answer the initial questions and more. Our contributions in this paper are: (a) we present a framework for analyzing epidemic spreading processes on mobile ad hoc networks, (b) using our framework, we are the first to derive the epidemic threshold for any mobility model under the SIS model, and (c) we show that the node velocity in mobility models does not affect the epidemic threshold. Additionally, we introduce a periodic mobility model and provide evaluation via our framework. We validate our theoretical predictions using a combination of simulated and synthetic mobility data, showing ultimately, our predictions accurately estimate the epidemic threshold of such systems.

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Section: Related Workmentioning

confidence: 99%

Epidemic Spread in Mobile Ad Hoc Networks: Determining the Tipping Point

Valler

Prakash

Tong³

et al. 2011

Networking 2011

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“…Virus spreading models [10][11][12] and control strategies in mobile environments attract much attention. So far, many efforts have been recently made to model virus spreading among computer network.…”

Section: Related Workmentioning

confidence: 99%

Influence of node mobility on virus spreading behaviors in multi-hop network

Liu

Jin

Wang

2016

J Wireless Com Network

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Multi-hop network has received growing attention recently with the widely use of wireless network communication technology. At the same time, the security of multi-hop network is facing more serve challenges. Unfortunately, classic techniques for computer virus spreading model cannot be applied to multi-hop network because of ignoring dynamic topology of network. In this paper, classic susceptible-infected (SI) model, susceptible-infectedsusceptible (SIS) model and susceptible-infected-removed (SIR) model are applied to multi-hop network based on random way-point (RWP) model, and contact duration of virus is introduced. Virus spreading behaviors are examined through contact duration of virus, communication radius of node, distribution density of node, and the number of initial infected nodes. Simulation results show that node mobility has significant effect on virus spreading behaviors. In particular, a special node speed that can lead network to appear the fastest-spreading virus phenomenon is found. The special speed is approximately equal to the ratio of communication radius of node to contact duration of virus. Distribution density of node and the number of initial infected nodes almost do not affect the special speed.

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“…In the literature of mobile ad hoc networks, Mickens et al [9,10] investigated queue-based propagation of malicious software under random waypoint model in mobile ad hoc networks. Valler et al [11] compute a general epidemic threshold in the SIS (Susceptible-Infectious-Susceptible) model and demonstrate that the epidemic threshold is not influenced by mobile node's speed.…”

Section: Related Workmentioning

confidence: 99%

Hop-by-Hop Worm Propagation with Carryover Epidemic Model in Mobile Sensor Networks

2015

Computers

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In the internet, a worm is usually propagated in a random multi-hop contact manner. However, the attacker will not likely select this random multi-hop propagation approach in a mobile sensor network. This is because multi-hop worm route paths to random vulnerable targets can be often breached due to node mobility, leading to failure of fast worm spread under this strategy. Therefore, an appropriate propagation strategy is needed for mobile sensor worms. To meet this need, we discuss a hop-by-hop worm propagation model in mobile sensor networks. In a hop-by-hop worm propagation model, benign nodes are infected by worm in neighbor-to-neighbor spread manner. Since worm infection occurs in hop-by-hop contact, it is not substantially affected by a route breach incurred by node mobility. We also propose the carryover epidemic model to deal with the worm infection quota deficiency that might occur when employing an epidemic model in a mobile sensor network. We analyze worm infection capability under the carryover epidemic model. Moreover, we simulate hop-by-hop worm propagation with carryover epidemic model by using an ns-2 simulator. The simulation results demonstrate that infection quota carryovers are seldom observed where a node's maximum speed is no less than 20 m/s.

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Analytical Models for Epidemics in Mobile Networks

Cited by 12 publications

References 17 publications

Epidemic Spread in Mobile Ad Hoc Networks: Determining the Tipping Point

Epidemic Spread in Mobile Ad Hoc Networks: Determining the Tipping Point

Influence of node mobility on virus spreading behaviors in multi-hop network

Hop-by-Hop Worm Propagation with Carryover Epidemic Model in Mobile Sensor Networks

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