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Multi-level framework for anomaly detection in social networking

Aditya Khamparia (School of Computer Science and Engineering, Lovely Professional University, Phagwara, India)
Sagar Pande (School of Computer Science and Engineering, Lovely Professional University, Phagwara, India)
Deepak Gupta (Maharaja Agrasen Institute of Technology, Delhi, India)
Ashish Khanna (Maharaja Agrasen Institute of Technology, Delhi, India)
Arun Kumar Sangaiah (School of Computing Science and Engineering, VIT University, Vellore, India)

Library Hi Tech

ISSN: 0737-8831

Article publication date: 3 January 2020

Issue publication date: 11 June 2020

286

Abstract

Purpose

The purpose of this paper is to propose a structured multilevel system that will distinguish the anomalies present in different online social networks (OSN).

Design/methodology/approach

Author first reviewed the related work, and then, the research model designed was explained. Furthermore, the details regarding Levels 1 and 2 were narrated.

Findings

By using the proposed technique, FScore obtained for Twitter and Facebook data set was 96.22 and 94.63, respectively.

Research limitations/implications

Four data sets were used for the experiment and the acquired outcomes demonstrate enhancement over the current existing frameworks.

Originality/value

This paper designed a multilevel framework that can be used to detect the anomalies present in the OSN.

Keywords

Citation

Khamparia, A., Pande, S., Gupta, D., Khanna, A. and Sangaiah, A.K. (2020), "Multi-level framework for anomaly detection in social networking", Library Hi Tech, Vol. 38 No. 2, pp. 350-366. https://doi.org/10.1108/LHT-01-2019-0023

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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