The Experts below are selected from a list of 69804 Experts worldwide ranked by ideXlab platform
Nan Li - One of the best experts on this subject based on the ideXlab platform.
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Malware Propagation in Online Social Networks: Nature, Dynamics, and Defense Implications
ACM Symposium on Information Computer and Communications Security, 2011Co-Authors: Guanhua Yan, Guanling Chen, Stephan Eidenbenz, Nan LiAbstract:Online Social Networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these Online Social Networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in Online Social Networks. Our study is based on a dataset collected from a real-world location-based Online Social network, which includes not only the Social graph formed by its users but also the users' activity events. We analyze the Social structure and user activity patterns of this network, and confirm that it is a typical Online Social network, suggesting that conclusions drawn from this specific network can be translated to other Online Social Networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, Social structure, and activity patterns on malware propagation in Online Social Networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in Online Social Networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of Online Social network malware and also shed light on how to defend against them effectively. Copyright 2011 ACM.
Guanhua Yan - One of the best experts on this subject based on the ideXlab platform.
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Malware Propagation in Online Social Networks: Nature, Dynamics, and Defense Implications
ACM Symposium on Information Computer and Communications Security, 2011Co-Authors: Guanhua Yan, Guanling Chen, Stephan Eidenbenz, Nan LiAbstract:Online Social Networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these Online Social Networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in Online Social Networks. Our study is based on a dataset collected from a real-world location-based Online Social network, which includes not only the Social graph formed by its users but also the users' activity events. We analyze the Social structure and user activity patterns of this network, and confirm that it is a typical Online Social network, suggesting that conclusions drawn from this specific network can be translated to other Online Social Networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, Social structure, and activity patterns on malware propagation in Online Social Networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in Online Social Networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of Online Social network malware and also shed light on how to defend against them effectively. Copyright 2011 ACM.
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AsiaCCS - Malware propagation in Online Social Networks: nature, dynamics, and defense implications
Proceedings of the 6th ACM Symposium on Information Computer and Communications Security - ASIACCS '11, 2011Co-Authors: Guanhua Yan, Guanling Chen, Stephan EidenbenzAbstract:Online Social Networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these Online Social Networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in Online Social Networks. Our study is based on a dataset collected from a real-world location-based Online Social network, which includes not only the Social graph formed by its users but also the users' activity events. We analyze the Social structure and user activity patterns of this network, and confirm that it is a typical Online Social network, suggesting that conclusions drawn from this specific network can be translated to other Online Social Networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, Social structure, and activity patterns on malware propagation in Online Social Networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in Online Social Networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of Online Social network malware and also shed light on how to defend against them effectively.
Stephan Eidenbenz - One of the best experts on this subject based on the ideXlab platform.
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Malware Propagation in Online Social Networks: Nature, Dynamics, and Defense Implications
ACM Symposium on Information Computer and Communications Security, 2011Co-Authors: Guanhua Yan, Guanling Chen, Stephan Eidenbenz, Nan LiAbstract:Online Social Networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these Online Social Networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in Online Social Networks. Our study is based on a dataset collected from a real-world location-based Online Social network, which includes not only the Social graph formed by its users but also the users' activity events. We analyze the Social structure and user activity patterns of this network, and confirm that it is a typical Online Social network, suggesting that conclusions drawn from this specific network can be translated to other Online Social Networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, Social structure, and activity patterns on malware propagation in Online Social Networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in Online Social Networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of Online Social network malware and also shed light on how to defend against them effectively. Copyright 2011 ACM.
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AsiaCCS - Malware propagation in Online Social Networks: nature, dynamics, and defense implications
Proceedings of the 6th ACM Symposium on Information Computer and Communications Security - ASIACCS '11, 2011Co-Authors: Guanhua Yan, Guanling Chen, Stephan EidenbenzAbstract:Online Social Networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these Online Social Networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in Online Social Networks. Our study is based on a dataset collected from a real-world location-based Online Social network, which includes not only the Social graph formed by its users but also the users' activity events. We analyze the Social structure and user activity patterns of this network, and confirm that it is a typical Online Social network, suggesting that conclusions drawn from this specific network can be translated to other Online Social Networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, Social structure, and activity patterns on malware propagation in Online Social Networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in Online Social Networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of Online Social network malware and also shed light on how to defend against them effectively.
Guanling Chen - One of the best experts on this subject based on the ideXlab platform.
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Malware Propagation in Online Social Networks: Nature, Dynamics, and Defense Implications
ACM Symposium on Information Computer and Communications Security, 2011Co-Authors: Guanhua Yan, Guanling Chen, Stephan Eidenbenz, Nan LiAbstract:Online Social Networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these Online Social Networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in Online Social Networks. Our study is based on a dataset collected from a real-world location-based Online Social network, which includes not only the Social graph formed by its users but also the users' activity events. We analyze the Social structure and user activity patterns of this network, and confirm that it is a typical Online Social network, suggesting that conclusions drawn from this specific network can be translated to other Online Social Networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, Social structure, and activity patterns on malware propagation in Online Social Networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in Online Social Networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of Online Social network malware and also shed light on how to defend against them effectively. Copyright 2011 ACM.
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AsiaCCS - Malware propagation in Online Social Networks: nature, dynamics, and defense implications
Proceedings of the 6th ACM Symposium on Information Computer and Communications Security - ASIACCS '11, 2011Co-Authors: Guanhua Yan, Guanling Chen, Stephan EidenbenzAbstract:Online Social Networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these Online Social Networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in Online Social Networks. Our study is based on a dataset collected from a real-world location-based Online Social network, which includes not only the Social graph formed by its users but also the users' activity events. We analyze the Social structure and user activity patterns of this network, and confirm that it is a typical Online Social network, suggesting that conclusions drawn from this specific network can be translated to other Online Social Networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, Social structure, and activity patterns on malware propagation in Online Social Networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in Online Social Networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of Online Social network malware and also shed light on how to defend against them effectively.
Haiyan Wang - One of the best experts on this subject based on the ideXlab platform.
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partial differential equations with robin boundary condition in Online Social Networks
Discrete and Continuous Dynamical Systems-series B, 2015Co-Authors: Haiyan Wang, Feng Wang, Kuai XuAbstract:In recent years, Online Social Networks such as Twitter, have become a major source of information exchange and research on information diffusion in Social Networks has been accelerated. Partial differential equations are proposed to characterize temporal and spatial patterns of information diffusion over Online Social Networks. The new modeling approach presents a new analytic framework towards quantifying information diffusion through the interplay of structural and topical influences. In this paper we develop a non-autonomous diffusive logistic model with indefinite weight and the Robin boundary condition to describe information diffusion in Online Social Networks. It is validated with a real dataset from an Online Social network, Digg.com. The simulation shows that the logistic model with the Robin boundary condition is able to more accurately predict the density of influenced users. We study the bifurcation, stability of the diffusive logistic model with heterogeneity in distance. The bifurcation and stability results of the model information describe either information spreading or vanishing in Online Social Networks.
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diffusive logistic model towards predicting information diffusion in Online Social Networks
International Conference on Distributed Computing Systems Workshops, 2012Co-Authors: Feng Wang, Haiyan WangAbstract:Online Social Networks have recently become an effective and innovative channel for spreading information and influence among hundreds of millions of end users. Most of prior work either carried out empirical studies or focus on the information diffusion modeling in temporal dimension, little attempt has been given on understanding information diffusion over both temporal and spatial dimensions. In this paper, we propose a Partial Differential Equation (PDE), specifically, a Diffusive Logistic (DL) equation to model the temporal and spatial characteristics of information diffusion. We present the temporal and spatial patterns in a real dataset collected from a Social news aggregation site, Digg, and validate the proposed DL equation in terms of predicting the information diffusion process. Our experiment results show that the DL model is able to characterize and predict the process of information propagation in Online Social Networks. For example, for the most popular news with 24,099 votes in Digg, the average prediction accuracy of DL model over all distances during the first 6 hours is 92.08%. To the best of our knowledge, this paper is the first attempt to use PDE-based model to study the information diffusion process in both temporal and spatial dimensions in Online Social Networks.
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diffusive logistic model towards predicting information diffusion in Online Social Networks
arXiv: Social and Information Networks, 2011Co-Authors: Feng Wang, Haiyan WangAbstract:Online Social Networks have recently become an effective and innovative channel for spreading information and influence among hundreds of millions of end users. Many prior work have carried out empirical studies and proposed diffusion models to understand the information diffusion process in Online Social Networks. However, most of these studies focus on the information diffusion in temporal dimension, that is, how the information propagates over time. Little attempt has been given on understanding information diffusion over both temporal and spatial dimensions. In this paper, we propose a Partial Differential Equation (PDE), specifically, a Diffusive Logistic (DL) equation to model the temporal and spatial characteristics of information diffusion in Online Social Networks. To be more specific, we develop a PDE-based theoretical framework to measure and predict the density of influenced users at a given distance from the original information source after a time period. The density of influenced users over time and distance provides valuable insight on the actual information diffusion process. We present the temporal and spatial patterns in a real dataset collected from Digg Social news site, and validate the proposed DL equation in terms of predicting the information diffusion process. Our experiment results show that the DL model is indeed able to characterize and predict the process of information propagation in Online Social Networks. For example, for the most popular news with 24,099 votes in Digg, the average prediction accuracy of DL model over all distances during the first 6 hours is 92.08%. To the best of our knowledge, this paper is the first attempt to use PDE-based model to study the information diffusion process in both temporal and spatial dimensions in Online Social Networks.
