Mining (Social) Network Graphs to Detect Random Link Attacks

Shrivastava, Nisheeth; Majumder, Anirban; Rastogi, Rajeev

doi:10.1109/icde.2008.4497457

Cited by 71 publications

(46 citation statements)

References 18 publications

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“…"Mining (Social) Network Graphs to Detect Random Link Attacks (RLA)" by Nisheeth Shrivastava et al [13] analyses the static social network graph to detect RLA to find spammers by profiling properties of whom they send the messages to, instead of what they are sending. In an RLA, a group of malicious users attack a large, randomly selected set of victims and incorporates a large number of existing attacks (like email spams, telemarketing calls, etc.…”

Section: Related Workmentioning

confidence: 99%

CyberPsycho Attacks: Techniques, Causes, Effects and Recommendations to End-Users

Gupta¹,

Nene²

2016

IJCA

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From recent years, online social network services are mostly used for marketing, social, political, religious campaign more than just connecting to the friends. Now-a-days, government of most of the countries actively participate on network enabled platforms. Many researchers worldwide are working on social network analysis to extract information from social network services to increase productivity, awareness, learning as well as finding malicious links, phishing, spams & trending viral contents. The study in this paper focuses on causes and effects of persuasive messages based on current trending news and events which effectively may influence an individual's behavior. We name this effect as Cyberspace plus Psychology effect leading to CyberPsycho attacks. In CyberPsycho attacks an attacker uses cyber space & social network to affect or change attitude or behavior, and achieve certain goals to attain political, religious, economical, social gains. It motivates social media users towards a certain objective by spreading the persuasive messages in the form of texts, images or videos. The study is unique, valuable and compels the experts in academia, researchers, technologists and end-users to understand & acknowledge the serious impact of psychological, social and cultural aspects of internet addiction. In this paper, we propose three fundamental models to address and analyze the above concerns. These three proposed models to identify occurrence of cyberpsycho attacks is the major contribution of this paper.

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Section: Related Workmentioning

confidence: 99%

CyberPsycho Attacks: Techniques, Causes, Effects and Recommendations to End-Users

Gupta¹,

Nene²

2016

IJCA

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“…Shrivastava [13] proposed an algorithm that can identify fake nodes based on the neighborhood graph difference between normal nodes and fake nodes.…”

Section: Related Workmentioning

confidence: 99%

“…The attack that only uses certain background knowledge and doesn't "actively" change the graph is called passive attack, and the one "actively" changes the graph when social networks are collecting data is called active attack. Most current works can be categorized into two classes: to prevent passive attack [8,6,4,16,3,15,17] and to prevent active attack [13].…”

Section: Related Workmentioning

confidence: 99%

Personalized privacy protection in social networks

2010

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Due to the popularity of social networks, many proposals have been proposed to protect the privacy of the networks. All these works assume that the attacks use the same background knowledge. However, in practice, different users have different privacy protect requirements. Thus, assuming the attacks with the same background knowledge does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. In this paper, we introduce a framework which provides privacy preserving services based on the user's personal privacy requests. Specifically, we define three levels of protection requirements based on the gradually increasing attacker's background knowledge and combine the label generalization protection and the structure protection techniques (i.e. adding noise edge or nodes) together to satisfy different users' protection requirements. We verify the effectiveness of the framework through extensive experiments.

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“…Graph Mining: There exists lots of "graph mining" algorithms: subgraph discovery(e.g., [12], [13], gPrune [14], gApprox [15], gSpan [16], Subdue [17], ADI [18], CSV [19]), computing communities (eg., [20], DENGRAPH [21], METIS [22]), attack detection [23], with too many alternatives for each of the above tasks. They are not directly related to the focus of this paper which is the static and dynamic structures of real networks.…”

Section: Related Workmentioning

confidence: 99%

Patterns on the Connected Components of Terabyte-Scale Graphs

Kang

McGlohon

Akoglu

et al. 2010

2010 IEEE International Conference on Data Mining

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Abstract-How do connected components evolve? What are the regularities that govern the dynamic growth process and the static snapshot of the connected components? In this work, we study patterns in connected components of large, real-world graphs. First, we study one of the largest static Web graphs with billions of nodes and edges and analyze the regularities among the connected components using GFD(Graph Fractal Dimension) as our main tool. Second, we study several time evolving graphs and find dynamic patterns and rules that govern the dynamics of connected components. We analyze the growth rates of top connected components and study their relation over time. We also study the probability that a newcomer absorbs to disconnected components as a function of the current portion of the disconnected components and the degree of the newcomer. Finally, we propose a generative model that explains both the dynamic growth process and the static regularities of connected components.

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Mining (Social) Network Graphs to Detect Random Link Attacks

Cited by 71 publications

References 18 publications

CyberPsycho Attacks: Techniques, Causes, Effects and Recommendations to End-Users

CyberPsycho Attacks: Techniques, Causes, Effects and Recommendations to End-Users

Personalized privacy protection in social networks

Patterns on the Connected Components of Terabyte-Scale Graphs

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