Visualization of information diffusion model in future internet

Limsaiprom, Prajit; Tantatsanawong, Panjai

doi:10.1145/1930286.1930300

Cited by 1 publication

(1 citation statement)

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“…The result of correlation analysis between usage of online social networks and virus distribution was significant at the 0.01 level (2-tailed) [1] and when the data mining system was developed with a cluster algorithm by distance measure and K-Means clustering method to analyze a large amount of IDS log data of online social networks to discover unknown online social networks anomaly and cyber-attacks patterns. The analysis was computed the model and evaluated with test set presented four attackers' patterns (i) attackers from googalz attempted to execute an arbitrary program on infected systems, (ii) an anomaly pattern came from facebook, imeem, youtube, and hi5, (iii) remote attacker from facebook could gain control of vulnerable systems, and (iv) Denial of Service came from skype and msn [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Security Visualization Analytics Model in Online Social Networks Using Data Mining and Graph-based Structure Algorithms

Limsaiprom¹,

Praneetpolgrang²,

Subsermsri³

2014

IJITCS

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Abstract-The rise of the Internet accelerates the creation of various large-scale online social networks, which can be described the relationships and activities between human beings. The online social networks relationships in real world are too big to present with useful information to identify the criminal or cyber-attacks. This research proposed new information security analytic model for online social networks, which called Security Visualization Analytics (SVA) Model. SVA Model used the set of algorithms (1) Graph-based Structure algorithm to analyze the key factors of influencing nodes about density, centrality and the cohesive subgroup to identify the influencing nodes of anomaly and attack patterns (2) Supervised Learning with oneR classification algorithm was used to predict new links from such influencing nodes in online social networks on discovering surprising links in the existing ones of influencing nodes, which nodes in online social networks will be linked next from the attacked influencing nodes to monitor the risk. The results showed 42 influencing nodes of anomaly and attack patterns and can be predict 31 new links from such nodes were achieved by SVA Model with the accuracy of confidence level 95.0%. The new proposed model and results illustrated SVA Model was significance analysis. Such understanding can lead to efficient implementation of tools to links prediction in online social networks. They could be applied as a guide to further investigate of social networks behavior to improve the security model and notify the risk, computer viruses or cyber-attacks for online social networks in advance.

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Section: Introductionmentioning

confidence: 99%