Virus infection control in online social networks based on probabilistic communities

Abstract

SUMMARYWith the rapid progress of the Internet, more and more people socialize and make new friends through online social network sites and applications, such as Facebook, Twitter, and MSN Messenger. The number of users of these online social network sites and applications has increased significantly within a short period. Unfortunately, online social networks are often the platforms that propagate malicious software, such as viruses. The malicious software is spread by infected users who automatically send fake requests to other users. After accepting the fake requests, the users are infected. In a realistic environment, everyone can decide whether to accept or decline a request, and thus, we study how to construct and use community structures to efficiently control virus propagation in online social networks whose users each have a probability, namely, inclination, of accepting a request. In this paper, a community detection algorithm is proposed to detect communities in online social networks. In addition, a number of users are selected from the communities for patching and controlling virus propagation. Simulation results show that our proposed method provides good performance in terms of the number of distributed patches.

A novel organizing scheme of single topic user group based on trust chain model in social network

“…However, specific features of SNS, especially comprehensive interest and trust relationships among users, have been paid little attention in most existing approaches. Essentially, there are following rationales for topic user group organizing in this study: (1) topic is the key item which attracts users to form a closely related group for topic-oriented information sharing and communication; (2) interest is the critical criterion for evaluating whether a user has the possibility of joining in a group or not; and (3) trust relationship is a feasible and effective method for enlarging the scale of topic user group since the more users trust mutually, the more possibilities there might be for their sharing common interest in the topic. Accordingly, our scheme features topic impact evaluation, interest measurement, and trust chain calculation by integrating the above 3 aspects.…”

“…Then, for each topic Θ i ∈ Top, we propose the algorithm of single topic group organizing based on trust chain: [1] topic=Θ, Δ . U = ∅,U C = ∅,U O = ∅; [2] for N i ∈ SNS do [3] if EI N i Θ ð Þ≥μ then (C j = N i ) ∧ (U C ← C j ); [4] for C j ∈ U C do [5] for N i ∈ SNS ∧ N i ∉ U C ∧ ∃ Ω(N i , C j ) do [6] if T(N i , C j ) ≥ η then [7] if [9] p = 0; [10] while p ≠ n do [11] if EI E k Θ ð Þ p ≥ω ∨ MI E k jΘ ð Þ p ≥ω then p = p + 1; [12] else…”

A novel organizing scheme of single topic user group based on trust chain model in social network

“…However, specific features of SNS, especially comprehensive interest and trust relationships among users, have been paid little attention in most existing approaches. Essentially, there are following rationales for topic user group organizing in this study: (1) topic is the key item which attracts users to form a closely related group for topic-oriented information sharing and communication; (2) interest is the critical criterion for evaluating whether a user has the possibility of joining in a group or not; and (3) trust relationship is a feasible and effective method for enlarging the scale of topic user group since the more users trust mutually, the more possibilities there might be for their sharing common interest in the topic. Accordingly, our scheme features topic impact evaluation, interest measurement, and trust chain calculation by integrating the above 3 aspects.…”

“…Then, for each topic Θ i ∈ Top, we propose the algorithm of single topic group organizing based on trust chain: [1] topic=Θ, Δ . U = ∅,U C = ∅,U O = ∅; [2] for N i ∈ SNS do [3] if EI N i Θ ð Þ≥μ then (C j = N i ) ∧ (U C ← C j ); [4] for C j ∈ U C do [5] for N i ∈ SNS ∧ N i ∉ U C ∧ ∃ Ω(N i , C j ) do [6] if T(N i , C j ) ≥ η then [7] if [9] p = 0; [10] while p ≠ n do [11] if EI E k Θ ð Þ p ≥ω ∨ MI E k jΘ ð Þ p ≥ω then p = p + 1; [12] else…”

MalText: Finding Malicious Account in Social Network using Novel Regulizer in Dynamic CNN

“…Multiple studies proposed various techniques to find and categorize abnormalities using the characteristics. Another research investigated how to thwart the intruder's suspicious actions in a big OSN by creating a community detection algorithm [26].…”

DeepOSN: Bringing deep learning as malicious detection scheme in online social network

“…By computing the features, they construct various approaches to identify and classify anomalies. To hinder the suspicious activities of the intruder, a study explores a method by forming a community detection algorithm [14].…”

A novel organizing scheme of single topic user group based on trust chain model in social network

“…However, specific features of SNS, especially comprehensive interest and trust relationships among users, have been paid little attention in most existing approaches. Essentially, there are following rationales for topic user group organizing in this study: (1) topic is the key item which attracts users to form a closely related group for topic-oriented information sharing and communication; (2) interest is the critical criterion for evaluating whether a user has the possibility of joining in a group or not; and (3) trust relationship is a feasible and effective method for enlarging the scale of topic user group since the more users trust mutually, the more possibilities there might be for their sharing common interest in the topic. Accordingly, our scheme features topic impact evaluation, interest measurement, and trust chain calculation by integrating the above 3 aspects.…”

“…Then, for each topic Θ i ∈ Top, we propose the algorithm of single topic group organizing based on trust chain: [1] topic=Θ, Δ . U = ∅,U C = ∅,U O = ∅; [2] for N i ∈ SNS do [3] if EI N i Θ ð Þ≥μ then (C j = N i ) ∧ (U C ← C j ); [4] for C j ∈ U C do [5] for N i ∈ SNS ∧ N i ∉ U C ∧ ∃ Ω(N i , C j ) do [6] if T(N i , C j ) ≥ η then [7] if [9] p = 0; [10] while p ≠ n do [11] if EI E k Θ ð Þ p ≥ω ∨ MI E k jΘ ð Þ p ≥ω then p = p + 1; [12] else…”

MalText: Finding Malicious Account in Social Network using Novel Regulizer in Dynamic CNN

“…Multiple studies proposed various techniques to find and categorize abnormalities using the characteristics. Another research investigated how to thwart the intruder's suspicious actions in a big OSN by creating a community detection algorithm [26].…”

DeepOSN: Bringing deep learning as malicious detection scheme in online social network

“…By computing the features, they construct various approaches to identify and classify anomalies. To hinder the suspicious activities of the intruder, a study explores a method by forming a community detection algorithm [14].…”

A novel organizing scheme of single topic user group based on trust chain model in social network

“…However, specific features of SNS, especially comprehensive interest and trust relationships among users, have been paid little attention in most existing approaches. Essentially, there are following rationales for topic user group organizing in this study: (1) topic is the key item which attracts users to form a closely related group for topic-oriented information sharing and communication; (2) interest is the critical criterion for evaluating whether a user has the possibility of joining in a group or not; and (3) trust relationship is a feasible and effective method for enlarging the scale of topic user group since the more users trust mutually, the more possibilities there might be for their sharing common interest in the topic. Accordingly, our scheme features topic impact evaluation, interest measurement, and trust chain calculation by integrating the above 3 aspects.…”

“…Then, for each topic Θ i ∈ Top, we propose the algorithm of single topic group organizing based on trust chain: [1] topic=Θ, Δ . U = ∅,U C = ∅,U O = ∅; [2] for N i ∈ SNS do [3] if EI N i Θ ð Þ≥μ then (C j = N i ) ∧ (U C ← C j ); [4] for C j ∈ U C do [5] for N i ∈ SNS ∧ N i ∉ U C ∧ ∃ Ω(N i , C j ) do [6] if T(N i , C j ) ≥ η then [7] if [9] p = 0; [10] while p ≠ n do [11] if EI E k Θ ð Þ p ≥ω ∨ MI E k jΘ ð Þ p ≥ω then p = p + 1; [12] else…”

MalText: Finding Malicious Account in Social Network using Novel Regulizer in Dynamic CNN

“…Multiple studies proposed various techniques to find and categorize abnormalities using the characteristics. Another research investigated how to thwart the intruder's suspicious actions in a big OSN by creating a community detection algorithm [26].…”

DeepOSN: Bringing deep learning as malicious detection scheme in online social network

“…By computing the features, they construct various approaches to identify and classify anomalies. To hinder the suspicious activities of the intruder, a study explores a method by forming a community detection algorithm [14].…”