Deakin University
Browse

An efficient hybrid system for anomaly detection in social networks

Download (974.7 kB)
Version 3 2024-09-21, 10:56
Version 2 2024-06-20, 03:36
Version 1 2024-06-20, 03:35
journal contribution
posted on 2024-09-21, 10:56 authored by MS Rahman, S Halder, Md Ashraf UddinMd Ashraf Uddin, UK Acharjee
AbstractAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

History

Journal

Cybersecurity

Volume

4

Article number

10

Pagination

1-11

Location

Berlin, Germany

Open access

  • Yes

ISSN

2096-4862

eISSN

2523-3246

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

1

Publisher

Springer

Usage metrics

    Research Publications

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC