A security approach based on honeypots: Protecting Online Social network from malicious profiles

TL;DR

This paper proposes a security method using social honeypots to detect and analyze malicious profiles in online social networks, enhancing protection against cyber threats and identity breaches.

Contribution

It introduces a novel approach employing social honeypots to identify malicious users and develop classifiers for profile filtering and monitoring.

Findings

Honeypots successfully captured malicious profiles

Characteristics of malicious profiles were analyzed

Classifiers were created to filter malicious users

Abstract

In the recent years, the fast development and the exponential utilization of social networks have prompted an expansion of social Computing. In social networks users are interconnected by edges or links, where Facebook, twitter, LinkedIn are most popular social networks websites. Due to the growing popularity of these sites they serve as a target for cyber criminality and attacks. It is mostly based on how users are using these sites like Twitter and others. Attackers can easily access and gather personal and sensitive users information. Users are less aware and least concerned about the security setting. And they easily become victim of identity breach. To detect malicious users or fake profiles different techniques have been proposed like our approach which is based on the use of social honeypots to discover malicious profiles in it. Inspired by security researchers who used honeypots to observe and analyze malicious activity in the networks, this method uses social honeypots to trap malicious users. The two key elements of the approach are: (1) The deployment of social honeypots for harvesting information of malicious profiles. (2) Analysis of the characteristics of these malicious profiles and those of deployed honeypots for creating classifiers that allow to filter the existing profiles and monitor the new profiles.