Malware propagation in Online Social Networks

Faghani, Mohammad Reza; Saidi, Hossein

doi:10.1109/malware.2009.5403023

Cited by 48 publications

(31 citation statements)

References 14 publications

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“…Therefore, it is important to identify the sources of Trojans in order to analyse and remove them as early as possible. There have been many works studying the propagation of Trojans [43]- [46]. In this paper, we adopt the propagation model in [46], which is a spatialtemporal SIR model that takes into account the network topology and temporal dynamics of user activities.…”

Section: Simulations For Single Source Estimationmentioning

confidence: 99%

“…There have been many works studying the propagation of Trojans [43]- [46]. In this paper, we adopt the propagation model in [46], which is a spatialtemporal SIR model that takes into account the network topology and temporal dynamics of user activities. Furthermore, it considers characteristics of modern Trojans, security practices, and user behaviors.…”

Section: Simulations For Single Source Estimationmentioning

confidence: 99%

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Estimating Infection Sources in Networks Using Partial Timestamps

Tang

Tay

2018

IEEE Trans.Inform.Forensic Secur.

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We study the problem of identifying infection sources in a network based on the network topology, and a subset of infection timestamps. In the case of a single infection source in a tree network, we derive the maximum likelihood estimator of the source and the unknown diffusion parameters. We then introduce a new heuristic involving an optimization over a parametrized family of Gromov matrices to develop a single source estimation algorithm for general graphs. Compared with the breadth-first search tree heuristic commonly adopted in the literature, simulations demonstrate that our approach achieves better estimation accuracy than several other benchmark algorithms, even though these require more information like the diffusion parameters. We next develop a multiple sources estimation algorithm for general graphs, which first partitions the graph into source candidate clusters, and then applies our single source estimation algorithm to each cluster. We show that if the graph is a tree, then each source candidate cluster contains at least one source. Simulations using synthetic and real networks, and experiments using real-world data suggest that our proposed algorithms are able to estimate the true infection source(s) to within a small number of hops with a small portion of the infection timestamps being observed.

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Section: Simulations For Single Source Estimationmentioning

confidence: 99%

Section: Simulations For Single Source Estimationmentioning

confidence: 99%

Estimating Infection Sources in Networks Using Partial Timestamps

Tang

Tay

2018

IEEE Trans.Inform.Forensic Secur.

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“…Their evaluation on a real-world social graph of Flickr, with two known worms, Koobface and Mikeyy, indicated that the detection system can effectively detect OSN worm propagations in early stages when less than 0.13 % of users are infected. Faghani and Saidi (2009) propose a general model of propagation of Cross Site Scripting (XSS) worms in virtual social network. They examined the effect of the friend-visiting probability in such networks on the propagation of the worms.…”

Section: Diffusion and Epidemics In Social Networkmentioning

confidence: 99%

Cyber Warfare

2015

Advances in Information Security

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“…Malware propagation in unconventional networks such as scale-free networks [10], [11], wireless sensor networks [12], [13], cellular networks (using Multimedia Messaging Service (MMS) and Bluetooth) [2] as well as traditional networks has been studied in [3], [14]. Worm and spammer based attacks on social networks have recently led researchers to focus on the security of online social networks using simulated topologies and user activities such as in [15]- [17]. A recent focus of researchers has been the understanding of how information flows in social networks such as Facebook and Twitter [18]- [20] who have used this information to detect spammers in online social networks.…”

Section: Comparison With Related Workmentioning

confidence: 99%

Analysis of Malware Propagation in Twitter

Sanzgiri

Hughes

Upadhyaya

2013

2013 IEEE 32nd International Symposium on Reliable Distributed Systems

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Abstract-Malware propagation in social networks is a potential risk that has not been well-studied yet as there are no formal threat models for social networks. In this paper we investigate the vulnerability and cost of spreading malware via Twitter. Towards this end we present three specific attack scenarios targeted for Twitter and systematically analyze the cost of staging each of these attacks. Our analysis presents the first step for understanding the threats on the security of a class of social networks. We identify the attack related parameters and verify these parameters by testing the attack on a NetLogobased simulator. Our analysis indicates that the cost of staging attacks to infect users of Twitter is low and that the proposed attack scenarios are plausible. Further, even with a low degree of connectivity and a low probability of clicking links, Twitter and its structure can be exploited by such attacks to infect many users with malware.

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Malware propagation in Online Social Networks

Cited by 48 publications

References 14 publications

Estimating Infection Sources in Networks Using Partial Timestamps

Estimating Infection Sources in Networks Using Partial Timestamps

Cyber Warfare

Analysis of Malware Propagation in Twitter

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