Exploiting temporal complex network metrics in mobile malware containment

Tang, John; Mascolo, Cecilia; Musolesi, Mirco; Latora, Vito

doi:10.1109/wowmom.2011.5986463

Cited by 40 publications

(31 citation statements)

References 19 publications

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“…In this context, temporal network modeling and analysis of various temporal centrality measures (see Sect. IV I) can be used for designing strategies for containing the spread of malware in mobile devices [146].…”

Section: A Person-to-person Communicationmentioning

confidence: 99%

Temporal networks

2012

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A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names

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Section: A Person-to-person Communicationmentioning

confidence: 99%

Temporal networks

2012

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“…In time-varying networks the analytical study of contagion processes is hindered by the difficulties in dealing with the concurrent time scales of the contagion and network evolution processes. [34][35][36][37][38][39]. In the case of activity driven networks however it is possible to derive the mean-field level dynamical equations describing the contagion process by defining the activity block variable I t a and S t a that represent the number of infected and susceptible individuals, respectively, in the class of activity a at time t. From those quantity it is possible to derive the mean-field evolution of the number of infected individuals of class a at time t + 1 as…”

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confidence: 99%

Controlling Contagion Processes in Activity Driven Networks

et al. 2014

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The vast majority of strategies aimed at controlling contagion processes on networks considers the connectivity pattern of the system as either quenched or annealed. However, in the real world many networks are highly dynamical and evolve in time concurrently to the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for time-varying networks. We consider the removal/immunization of individual nodes according the their activity in the network and develop a block variable mean-field approach that allows the derivation of the equations describing the evolution of the contagion process concurrently to the network dynamic. We derive the critical immunization threshold and assess the effectiveness of the control strategies. Finally, we validate the theoretical picture by simulating numerically the information spreading process and control strategies in both synthetic networks and a large-scale, real-world mobile telephone call dataset.The spreading of infectious diseases, malwares, scientific ideas, memes are just few example of real world phenomena that can be modeled as contagion processes on networks [1][2][3][4]. It has long been acknowledged that network structures and connectivity patterns are a relevant factor in determining the properties of spreading processes. A number of strategies aimed at controlling them have been proposed with the aim of improving on the random removal strategies that originally defined the concept of herd immunity. Those strategies target nodes according the number of connections (degree), the k-core or the betweenness of nodes, just to mention a few examples [5,6]. The efficiency of each strategy is then measured by its effect on the contagion process when a fraction p of nodes is removed from the system. More precisely, the smaller is the fraction p of nodes removed in order to halt the contagion process and the more effective is the strategy. Although most real world networks show a high level of dynamic activity, the large majority of theoretical results concerning the control of contagion processes have been obtained by using a complete timescale separation between the contagion process, τ P , and the change in network's structure, τ G . In these approaches the dynamical process takes place in either static (τ P τ G ) or annealed (τ P τ G ) networks. However, when the two time scales are comparable these convenient approximations might introduce uncontrolled biases in the correct characterization of the dynamical properties of the contagion process . Here we investigate the effect of time-varying connectivity pattern of networks on contagion control strategies by considering the specific class of activity driven network models [23]. In particular, we consider the susceptibleinfected-susceptible (SIS) contagion model [30] and derive analytically its critical immunization threshold in three different control strategies. We also validate the findings obtained in synthetic networks by studying the effect of each strategy...

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“…Tang et al [92] studied the spreading of malwares via bluetooth and efficient way to contain them. They considered three different datasets: the MIT reality mining data mentioned before, the interaction between researchers at the University of Cambridge [93], and those between participants of a conference [94].…”

Section: Controlling Contagion Processes In Real Temporal Networkmentioning

confidence: 99%

Control Strategies of Contagion Processes in Time-Varying Networks

Karsai

Perra

2017

Theoretical Biology

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The vast majority of strategies aimed at controlling contagion processes on networks consider a timescale separation between the evolution of the system and the unfolding of the process. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently to the contagion phenomena. Here, we review the most commonly used immunization strategies on networks. In the first part of the chapter, we focus on controlling strategies in the limit of timescale separation. In the second part instead, we introduce results and methods that relax this approximation. In doing so, we summarize the main findings considering both numerical and analytically approaches in real as well as synthetic time-varying networks.

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Exploiting temporal complex network metrics in mobile malware containment

Cited by 40 publications

References 19 publications

Temporal networks

Temporal networks

Controlling Contagion Processes in Activity Driven Networks

Control Strategies of Contagion Processes in Time-Varying Networks

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