A Heuristic Reputation Based System to Detect Spam activities in a Social Networking Platform, HRSSSNP

TL;DR

This paper proposes a heuristic reputation system that models social networks as weighted graphs to detect spam and malicious activities by analyzing trust relationships and localized activity data.

Contribution

It introduces a novel reputation-based approach using weighted graphs and localized data-sets to identify spam activities in social networking platforms.

Findings

Effective detection of spam activities using trust-weighted graph analysis

Improved accuracy in identifying malicious nodes in social networks

Enhanced security measures for social networking platforms

Abstract

The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the interactions on a social networking website. A considerable proportion of the crimes that occur are initiated through a social networking platform [5]. Almost 33% of the crimes on the internet are initiated through a social networking website [5]. Moreover activities like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a result preventing interactions with malicious intent and spam activities becomes crucial. This work attempts to detect the same in a social networking platform by considering a social network as a weighted graph wherein each node, which represents an individual in the social network, stores activities of other nodes with respect to itself in an optimized format which is referred to as localized data-set. The weights associated with the edges in the graph represent the trust relationship between profiles. The weights of the edges along with the localized data-set is used to infer whether nodes in the social network are compromised and are performing spam or malicious activities.