Virus Propagation in Heterogeneous Bluetooth Networks with Human Behaviors

Jackson, Jennifer T.; Creese, Sadie

doi:10.1109/tdsc.2012.72

Cited by 30 publications

(15 citation statements)

References 12 publications

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“…In this section, we propose a feasible algorithm to compute the propagation power of virus in large-scale dynamic networks based on dynamic community mining algorithm in [26], which can efficiently evaluate the propagation risk of virus in a community network.…”

Section: Calculating Propagation Power In Dynamic Networkmentioning

confidence: 99%

“…But in a large-scale dynamic network, immunization strategy should cover 80% of nodes by stochastic immunization strategy or the whole topology should be known using a special immunization strategy [13,14]. Through local strategies, once a node finds itself infected, it sends an antivirus message to others immediately and the virus can be cleared accordingly [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Hui et al [24,25] propose a message dissemination mechanism through social community, but the mechanism requires that the virus is locally detected. Jackson and Creese [26] study virus propagation from the perspective of human behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Virus propagation power of the dynamic network

Huang

Han

et al. 2013

J Wireless Com Network

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With the development of mobile networks, propagation characteristics and defense mechanism of the virus have attracted increasing research attention. But current researches are mainly concerned with static network topology or community structure and some studies focus on characteristics of virus and malware. Little attention is paid to the influence of dynamic changes of network topology to virus propagation. Meanwhile, many studies focus on the threshold rate of the infection (or the immunization rate) for virus outbreak. In this paper, we present a new way to assess and restrain virus propagation by proposing the concepts of propagation power and propagation structure. Three basic propagation structures are presented through which the infection risk can be quantified, and a framework is proposed to assess the impact of virus propagation in different network structures. The relationship between the speed of virus propagation and network structure is also explored. An algorithm is designed to assess the propagation power in a dynamic network with no need to redetect the community under the dynamic network which may significantly improve the efficiency of assessment in a large-scale dynamic network. This study offers a feasible approach for quantifying the risk of virus infection in the network community which is valuable for designing and optimizing the virus defense systems.

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Section: Calculating Propagation Power In Dynamic Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Virus propagation power of the dynamic network

Huang

Han

et al. 2013

J Wireless Com Network

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“…Most of these works are based on continuous mathematical tools such as systems of ordinary differential equations (see [5][6][7][8][9][10][11][12]). These type of models does not take into account local interactions between the smartphones, assume that the elements forming the network are distributed homogeneously and that all are connected with one another, and therefore, they are unable to simulate the individual dynamic of each of the smartphones of the network.…”

Section: Introductionmentioning

confidence: 99%

Worm Propagation Modeling Considering Smartphones Heterogeneity and People Mobility

García¹,

Lárraga²,

Álvarez-Icaza

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)

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Abstract-In recent years, the worldwide market for smartphones has grown dramatically. The kind of information stored in these devices makes them an attractive target for malware writers. Consequently, modeling of worm propagation in smartphones in order to predict the side effects of a new threat and understand the complex behavior of the modeled malware has received significant attention. One of the possible mechanisms for malware spreading are Bluetooth antennas, where the malware infects devices in its proximity as biological viruses do. Due to this strong similarity in the behaviors of self-replicating and propagation between mobile malware and biological viruses, most investigations of malware propagation in smartphones focus predominately on modeling its propagation dynamics by employing the classical epidemic theories in epidemiology. Cellular Automata (CA) models have emerged recently as a promising alternative to characterize worm propagation and understand its behavior. However, in the most of the existing CA models for mobile malware, it is assumed that all smartphones are homogeneous and that transmission time of the worm is one time cycle. In this work, a mathematical model to study the spatio-temporal propagation dynamics of Bluetooth worms based on CA and the compartmental epidemiological models is introduced. The model considers the local interactions between the smartphones and is able to simulate the individual dynamic of each device and the effect of mobility of their users on the infection propagation.

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“…This fact is mainly due to the following: (1) Its leader position: Android holds 80% of the total market share in tablet and smartphone devices; (2) Its open-source nature; (3) The loose security policies for apps which make possible the distribution of malware through both the Android Play Store and the unofficial stores. In this sense, the 97% of malware threats target the Android operating system.…”

Section: Introductionmentioning

confidence: 99%

A Cellular Automata Model for Mobile Worm Propagation

Rey

Encinas

Vaquero

et al. 2015

Lecture Notes in Computer Science

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Abstract. The mobile devices, and especially the smartphones, are exposed to the malicious effects of malware. In this sense the study, simulation and control of epidemic processes due to malware is an important issue. The main goal of this work is to introduce a new mathematical model to study the spread of a mobile computer worm. Its dynamic is governed by means of two cellular automata based on logic transition functions. Some computer simulations are shown and analyzed in order to determine how a mobile worm might spread under different conditions.

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Virus Propagation in Heterogeneous Bluetooth Networks with Human Behaviors

Cited by 30 publications

References 12 publications

Virus propagation power of the dynamic network

Virus propagation power of the dynamic network

Worm Propagation Modeling Considering Smartphones Heterogeneity and People Mobility

A Cellular Automata Model for Mobile Worm Propagation

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