Fake Profile Detection Research Articles

Fake profile detection in multimedia big data on online social networks

The popularity of online social networks like Facebook and Twitter has become the regular way of communication and interaction. Due to the popularity of such networks, the attackers try to reveal suspicious behaviour in the form of fake profile. To stop fake profile, various approaches are proposed in the recent years. The focus of recent work is to implement a machine learning technique to detect fake profile on Facebook platform by analysing public as well as private features. In this paper, a machine learning-based approach is proposed for detecting suspicious profiles for tapping and tainting multimedia big data on Facebook. Multimedia big data is a type of dataset in which the data is heterogeneous, human centric and has more media related contents with huge volumes like text, audio and video generated in different online social network. The experimental result of our work using content-based and profile-based features delivers first rate performance as compared to other approaches.

Fake profile detection in multimedia big data on online social networks

The popularity of online social networks like Facebook and Twitter has become the regular way of communication and interaction. Due to the popularity of such networks, the attackers try to reveal suspicious behaviour in the form of fake profile. To stop fake profile, various approaches are proposed in the recent years. The focus of recent work is to implement a machine learning technique to detect fake profile on Facebook platform by analysing public as well as private features. In this paper, a machine learning-based approach is proposed for detecting suspicious profiles for tapping and tainting multimedia big data on Facebook. Multimedia big data is a type of dataset in which the data is heterogeneous, human centric and has more media related contents with huge volumes like text, audio and video generated in different online social network. The experimental result of our work using content-based and profile-based features delivers first rate performance as compared to other approaches.

Fake Profile Identification in Online Social Networks

There is a tremendous increase in technologies these days.. Mobiles are becoming smart. Technology is associated with online social networks which has become a part in every one’s life in making new friends and keeping friends , their interests are known easier. But this increase in networking online make many problems like faking their profiles, online impersonation having become more and more in present days. Users are fed with more unnecessary knowledge during surfing which are posted by fake users. Researches have observed that 20% to 40% profiles in online social networks like facebook are fake profiles. Thus this detection of fake profiles in online social networks results into solution using frameworks.

Automatic Detection of Fake Profiles in Online Social Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Fake profile detection techniques in large-scale online social networks: A comprehensive review

In the present era, online social networks are the most popular and rapid information propagation applications on the Internet. People of all ages spend most of their time on social networking sites. Huge volumes of data are being created and shared through social networks around the world. These interests have given rise to illegitimate users who engage in fraudulent activities against social network users. On social networks, fake profile creation is considered to cause more harm than any other form of cyber crime. This crime has to be detected even before the user is notified about the fake profile creation. Many algorithms and methods, most of which use the huge volume of unstructured data generated from social networks, have been proposed for the detection of fake profiles. This study presents a survey of the existing and latest technical work on fake profile detection.

Implications of Various Fake Profile Detection Techniques in Social Networks

In the recent years, the fast development and the exponential utilization of social networks has prompted an expansion of social Computing. In social networks users are interconnected by edges or links. Facebook, twitter, linkedin are most popular social networks websites. In this paper focus is made on Facebook for detection of fake profile. Facebook is most used social networking site in which user can share messages, images and videos also users may add number of friends in their personal profiles. But it is difficult to find out whether the new person is genuine or not. May be it could be a malicious user. To detect malicious users or fake profiles different techniques has been proposed. In this paper an attempt has been made to analysis various existing techniques that includes comparison in perspective of various applications mapping various performance parameters.