Modeling and validation of SMS worm propagation over social networks

Xiao, Xi; Fu, Peng; Li, Qing; Hu, Guangwu; Jiang, Yong

doi:10.1016/j.jocs.2017.05.011

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…2, pp. 175-184 © The Institution of Engineering and Technology 2019 attack [6][7][8]. Second, the reason for the current popularity of virtual currency is that many worms, with 'mining' programs, illegally use the resources of other people's computers for calculation.…”

Section: Related Workmentioning

confidence: 99%

Novel non‐linear dynamics P2P network worm propagation and immune model

Liu

2020

IET Information Security

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Section: Related Workmentioning

confidence: 99%

Novel non‐linear dynamics P2P network worm propagation and immune model

Liu

2020

IET Information Security

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“…spread widely and rapidly. Use of social networks varies from the prediction of customer behavior Goel and Goldstein (2013) to understanding the sms wormhole propagation Xiao et al (2017). One of the important phenomena of social networks is the information diffusion and this means that if a user has some information then he or she tends to share it with his or her neighbors.…”

Section: Abstract Social Networkmentioning

confidence: 99%

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Banerjee¹

2020

Preprint

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Implicit Social Network is a connected social structure among a group of persons, where two of them are linked if they have some common interest. One real-life example of such networks is the implicit social network among the customers of an online commercial house, where there exist an edge between two customers if they like similar items. Such networks are often useful for different commercial applications such as target advertisement, viral marketing, etc. In this article, we study two fundamental problems in this direction. The first one is that, given the user-item rating data of an E-Commerce house, how we can design implicit social networks among its users and the second one is at the time of designing itself can we obtain the connectivity information among the users. Formally, we call the first problem as the Implicit User Network Design Problem and the second one as Implicit User Network Design with Connectivity Checking Problem. For the first problem, we propose three different algorithms, namely 'Exhaustive Search Approach', 'Clique Addition Approach', and 'Matrix Multiplication-Based Approach'. For the second problem, we propose two different approaches. The first one is the sequential approach: designing and then connectivity checking, and the other one is a concurrent approach, which is basically an incremental algorithm that performs designing and connectivity checking simultaneously. Proposed methodologies have experimented with three publicly available rating network datasets such as Flixter, Movielens, and Epinions. Reported computational time shows that the 'Clique Addition Approach' is the fastest one for designing the implicit social network. For designing and connectivity check-The author is supported by the Post Doctoral Fellowship Grant provided by Indian Institute of Technology Gandhinagar (Project No. MIS/IITGN/PD-SCH/201415/006). A small part of this study has been previously published as Banerjee et al. (2017).

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“…Deterministic models are usually global (that is, they suppose that all devices have the same characteristics and the contact topology is homogeneous) and, consequently, they are based on-deterministic-ordinary differential equations [11]). On the other hand, stochastic models can be also global (and based on stochastic differential equations), although the great majority follows the individual paradigm [12,13] and, consequently, takes into account particular characteristics of devices. All of them are compartmental models where the total population of devices is classified into different classes or compartments (depending on the epidemiological state).…”

Section: Introductionmentioning

confidence: 99%

Propagation of the Malware Used in APTs Based on Dynamic Bayesian Networks

2021

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Malware is becoming more and more sophisticated these days. Currently, the aim of some special specimens of malware is not to infect the largest number of devices as possible, but to reach a set of concrete devices (target devices). This type of malware is usually employed in association with advanced persistent threat (APT) campaigns. Although the great majority of scientific studies are devoted to the design of efficient algorithms to detect this kind of threat, the knowledge about its propagation is also interesting. In this article, a new stochastic computational model to simulate its propagation is proposed based on Bayesian networks. This model considers two characteristics of the devices: having efficient countermeasures, and the number of infectious devices in the neighborhood. Moreover, it takes into account four states: susceptible devices, damaged devices, infectious devices and recovered devices. In this way, the dynamic of the model is SIDR (susceptible–infectious–damaged–recovered). Contrary to what happens with global models, the proposed model takes into account both the individual characteristics of devices and the contact topology. Furthermore, the dynamics is governed by means of a (practically) unexplored technique in this field: Bayesian networks.

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Modeling and validation of SMS worm propagation over social networks

Cited by 13 publications

References 27 publications

Novel non‐linear dynamics P2P network worm propagation and immune model

Novel non‐linear dynamics P2P network worm propagation and immune model

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Propagation of the Malware Used in APTs Based on Dynamic Bayesian Networks

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