A sneak into the Devils Colony - Fake Profiles in Online Social Networks
📝 Abstract
Online Social Networks (OSNs) play an important role for internet users to carry out their daily activities like content sharing, news reading, posting messages, product reviews and discussing events etc. At the same time, various kinds of spammers are also equally attracted towards these OSNs. These cyber criminals including sexual predators, online fraudsters, advertising campaigners, catfishes, and social bots etc. exploit the network of trust by various means especially by creating fake profiles to spread their content and carry out scams. All these malicious identities are very harmful for both the users as well as the service providers. From the OSN service provider point of view, fake profiles affect the overall reputation of the network in addition to the loss of bandwidth. To spot out these malicious users, huge manpower effort and more sophisticated automated methods are needed. In this paper, various types of OSN threat generators like compromised profiles, cloned profiles and online bots (spam bots, social bots, like bots and influential bots) have been classified. An attempt is made to present several categories of features that have been used to train classifiers in order to identify a fake profile. Different data crawling approaches along with some existing data sources for fake profile detection have been identified. A refresher on existing cyber laws to curb social media based cyber crimes with their limitations is also presented.
💡 Analysis
Online Social Networks (OSNs) play an important role for internet users to carry out their daily activities like content sharing, news reading, posting messages, product reviews and discussing events etc. At the same time, various kinds of spammers are also equally attracted towards these OSNs. These cyber criminals including sexual predators, online fraudsters, advertising campaigners, catfishes, and social bots etc. exploit the network of trust by various means especially by creating fake profiles to spread their content and carry out scams. All these malicious identities are very harmful for both the users as well as the service providers. From the OSN service provider point of view, fake profiles affect the overall reputation of the network in addition to the loss of bandwidth. To spot out these malicious users, huge manpower effort and more sophisticated automated methods are needed. In this paper, various types of OSN threat generators like compromised profiles, cloned profiles and online bots (spam bots, social bots, like bots and influential bots) have been classified. An attempt is made to present several categories of features that have been used to train classifiers in order to identify a fake profile. Different data crawling approaches along with some existing data sources for fake profile detection have been identified. A refresher on existing cyber laws to curb social media based cyber crimes with their limitations is also presented.
📄 Content
A sneak into the Devil’s Colony- Fake Profiles in Online Social Networks Mudasir Ahmad Wani*,a Suraiya Jabina
mudasirwanijmi@gmail.com sjabin@jmi.ac.in
a Department of Computer
Jamia Millia Islamia, New Delhi
Abstract – Online Social Networks (OSNs) play an important role for internet users to carry out their daily activities like content sharing, news reading, posting messages, product reviews and discussing events etc. At the same time, various kinds of spammers are also equally attracted towards these OSNs. These cyber criminals including sexual predators, online fraudsters, advertising campaigners, catfishes, and social bots etc. exploit the network of trust by various means especially by creating fake profiles to spread their content and carry out scams. All these malicious identities are very harmful for both the users as well as the service providers. From the OSN service providers’ point of view, fake profiles affect the overall reputation of the network in addition to the loss of bandwidth. To spot out these malicious users, huge manpower effort and more sophisticated automated methods are needed. In this paper, various types of OSN threat generators like compromised profiles, cloned profiles and online bots (spam-bots, social-bots, like-bots and influential-bots) have been classified. An attempt is made to present several categories of features that have been used to train classifiers in order to identify a fake profile. Different data crawling approaches along with some existing data sources for fake profile detection have been identified. A refresher on existing cyber laws to curb social media based cybercrimes with their limitations is also presented. Keywords – Online Social Network Analysis (OSNA), Online Social Networks (OSNs), Privacy & Security, Online Social Bots, Fake profiles, Facebook Immune System, Cyber law.
- INTRODUCTION
Online Social Network Analysis (OSNA) is considered as one of the most emerging research fields.
An online social network (OSN) is the grouping of nodes (individuals, actors, organizations, nations,
states or WebPages etc.) around the world connected by a set of links (relationships, interactions,
distances, hyperlinks etc). Since its inception, OSN has changed the way people think, express, and
socialize with outside world. For example to buy a new product, people find it better to look for Google
reviews rather than taking a friend’s advice. Currently there are umpteen Social networking sites like
Facebook, Twitter, Google+, Flicker, LinkedIn, Hello etc. and almost every individual is member of
one of these OSNs. These OSNs are growing rapidly in terms of the number of users and the number
of connections across the different geographies. Although, OSNs are gaining universal popularity but
it brings number of security and privacy challenges like spam, scam, phishing, clickjacking harassing
or stalking an individual or a group, defamation, identity theft, third party personal information
disclosure etc. Since user’s personal, professional, social and political data is cluttered at a single place
which equally attracts social spammers (cyber criminals) towards these OSNs which can be very
harmful for both users as well as service providers. These cybercriminals use identity theft attacks,
creating fake profiles or launching automated crawling against a number of popular social networking
sites. Other reasons for creating fake profiles include advertising and campaigning, defaming a person,
social engineering, fun and entertainment, data collection for research/ specialized marketing, fake
traffic for blogs or websites etc.
OSNs provide huge amount of user generated content easily, so it’s always under attack of spammers. Mostly the aim of these cyber criminals is to steal the user’s personal, professional, political, social or financial information by exposing the users with unwanted information on the web likes, pornography etc. in order to deceive them. There are number of methods by which the users’ data can be hacked by these adversaries, and creating fake profiles to perform malicious activities on OSNs is one of the mostly employed methods. From the users’ point of view, personal, professional and even financial data is no
more secure. Figure 1 provides a quick view of various kinds of fake profiles and several other kinds of profiles found in different online social networks. Real profiles have to be categorised into compromised and no
This content is AI-processed based on ArXiv data.