Anomaly detection in online social networks

Savage, David; Zhang, Xiuzhen; Yu, Xinghuo; Chou, Pauline Lienhua; Wang, Qingmai

doi:10.1016/j.socnet.2014.05.002

Cited by 233 publications

(136 citation statements)

References 67 publications

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“…David Savage, et.al (2014) proposed that in this paper [13] the existing computational techniques are surveyed for detecting anomalies in online social networks. The paper characterizes anomalies as being either static or dynamic, and as being labeled or unlabeled, and survey methods for detecting these diverse types of anomalies.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

An Effective Technique to Identify Anomalous Accounts on Social Networks using Bloom Filter

kaur¹,

Kaur²

2017

IJCA

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The anomaly detection is the technique which is applied to detect malicious activities from the social network data. The existing technique is based on to classify the Facebook accounts into three classes which are fake, genuine and moderate. To increase accuracy of account classification is increased when bloom filter is being applied in the algorithm. The bloom filter is the algorithm which learns from the previous experiences and drive new values. When the bloom filter is applied the accounts are classified into two classes. The simulation is being performed in MATLAB and it is being analyzed that accuracy is increased and execution time is reduced.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

An Effective Technique to Identify Anomalous Accounts on Social Networks using Bloom Filter

kaur¹,

Kaur²

2017

IJCA

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“…Star-networks are also associated with financial fraud (Šubelj, Furlan, Bajec, 2011), academic fraud (Callaway, 2011), and terrorist activities (Reid et al, 2005;Krebs, 2002). In a recent paper, Savage et al (2014) categorize star-networks and near-star-networks in an effort to make their online detection more effective. Mutual and common knowledge, especially about problematic behavior by the hub of a community, is greatly enhanced by the gossip that occurs in Euclidean connections in that community.…”

Section: Figure 4 Symmetric But Non-euclidean Trustmentioning

confidence: 99%

The Topology of Communities of Trust

Alfano

2016

RSR

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“…Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. The detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space [16].…”

Section: Detection Of Terrorist Groups:-mentioning

confidence: 99%

COMMUNITY DETECTION on SOCIAL MEDIA using GRAPH BASED APPROACH.

Raman¹,

Kanal.²

2016

IJAR

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Social media has followed an exponential graph over the past few years with incorporating features which at one time seemed impossible. The social media has had an enduring effect on the thought process of the general populace. With the diverse nature of the population which take part in the daily chatting, tagging, posting and uploading on the virtual world, the study of such coalesce of communities. This paper aims at the mining and analysis of the communities with focus on the techniques used for the detection process. We discuss four methods of detection, beginning with the node-centric moving on to group centric, then to network centric and concluding with hierarchy centric method of detection. This paper also briefly discusses the applications of community detection in varied fields.Copy Right, IJAR, 2016,. All rights reserved. …………………………………………………………………………………………………….... Introduction:-The past decade has witnessedthe rapid development of social networking sites which has empowered new ways of collaboration and communication. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale [2]. Hence, study of social network is of great importance in sociology, biology and computer science. Social network analysis is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. Social network analysis provides both a visual and a mathematical analysis of human relationships. A valuable tool in the analysis of large complex networks is community detection. Community Detection:-Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group [1]. There are two types of communities in social networksExplicit groups which are formed as a result of conscious human decision. Implicit groups which emerge from interactions and activities of users. Often communities are defined with respect to a graph, which consists of set of objects called vertices (V) and their relations called as edges (E). Therefore, according to computer science, community detection is identifying a group of vertices that are more densely connected to each other than the rest of the network [1]. Figure below shows a network with three communities.

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Anomaly detection in online social networks

Cited by 233 publications

References 67 publications

An Effective Technique to Identify Anomalous Accounts on Social Networks using Bloom Filter

An Effective Technique to Identify Anomalous Accounts on Social Networks using Bloom Filter

The Topology of Communities of Trust

COMMUNITY DETECTION on SOCIAL MEDIA using GRAPH BASED APPROACH.

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