Supervised Machine Learning Based Anomaly Detection in Online Social Networks

Chi Leng Che; Ting Kai Hwang; Yung Ming Li

doi:10.1007/978-3-031-45645-9_8

Supervised Machine Learning Based Anomaly Detection in Online Social Networks

Chi Leng Che, Ting Kai Hwang^*, Yung Ming Li

^*Corresponding author for this work

Institute of Information Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

With the rapid development of online social networks (OSNs), a huge number of information provided by some entities around the world are well dispersed in OSNs every day. Most of those are useful but not all as anomalous entities utilize anomaly users to spread malicious content (like spam or rumors to achieve their pecuniary or political aims). In this paper, we propose a mechanism to detect such anomaly users according to the user profile and tweet content of each user. We design several features related to near-duplicate content (including lexical similarity and semantic similarity) to enhance the precision of detecting anomaly users. Utilizing the data by public honeypot dataset, the proposed approach deals with supervised learning approach to carry out the detection task.

Original language	English
Title of host publication	Information Systems and Technologies - WorldCIST 2023
Editors	Alvaro Rocha, Hojjat Adeli, Gintautas Dzemyda, Fernando Moreira, Valentina Colla
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	85-91
Number of pages	7
ISBN (Print)	9783031456442
DOIs	https://doi.org/10.1007/978-3-031-45645-9_8
State	Published - 2024
Event	11th World Conference on Information Systems and Technologies, WorldCIST 2023 - Pisa, Italy Duration: 4 Apr 2023 → 6 Apr 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	800
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	11th World Conference on Information Systems and Technologies, WorldCIST 2023
Country/Territory	Italy
City	Pisa
Period	4/04/23 → 6/04/23

Keywords

Anomaly Detection
Near-Duplicate Computing
Online Social Network
Supervised Machine Learning

Access to Document

10.1007/978-3-031-45645-9_8

Cite this

Che, C. L., Hwang, T. K., & Li, Y. M. (2024). Supervised Machine Learning Based Anomaly Detection in Online Social Networks. In A. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & V. Colla (Eds.), Information Systems and Technologies - WorldCIST 2023 (pp. 85-91). (Lecture Notes in Networks and Systems; Vol. 800). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-45645-9_8

Che, Chi Leng ; Hwang, Ting Kai ; Li, Yung Ming. / Supervised Machine Learning Based Anomaly Detection in Online Social Networks. Information Systems and Technologies - WorldCIST 2023. editor / Alvaro Rocha ; Hojjat Adeli ; Gintautas Dzemyda ; Fernando Moreira ; Valentina Colla. Springer Science and Business Media Deutschland GmbH, 2024. pp. 85-91 (Lecture Notes in Networks and Systems).

@inproceedings{c61d029ad9794ebaae5fd6dfa2a47fd4,

title = "Supervised Machine Learning Based Anomaly Detection in Online Social Networks",

abstract = "With the rapid development of online social networks (OSNs), a huge number of information provided by some entities around the world are well dispersed in OSNs every day. Most of those are useful but not all as anomalous entities utilize anomaly users to spread malicious content (like spam or rumors to achieve their pecuniary or political aims). In this paper, we propose a mechanism to detect such anomaly users according to the user profile and tweet content of each user. We design several features related to near-duplicate content (including lexical similarity and semantic similarity) to enhance the precision of detecting anomaly users. Utilizing the data by public honeypot dataset, the proposed approach deals with supervised learning approach to carry out the detection task.",

keywords = "Anomaly Detection, Near-Duplicate Computing, Online Social Network, Supervised Machine Learning",

author = "Che, {Chi Leng} and Hwang, {Ting Kai} and Li, {Yung Ming}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 11th World Conference on Information Systems and Technologies, WorldCIST 2023 ; Conference date: 04-04-2023 Through 06-04-2023",

year = "2024",

doi = "10.1007/978-3-031-45645-9_8",

language = "English",

isbn = "9783031456442",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "85--91",

editor = "Alvaro Rocha and Hojjat Adeli and Gintautas Dzemyda and Fernando Moreira and Valentina Colla",

booktitle = "Information Systems and Technologies - WorldCIST 2023",

address = "Germany",

}

Che, CL, Hwang, TK & Li, YM 2024, Supervised Machine Learning Based Anomaly Detection in Online Social Networks. in A Rocha, H Adeli, G Dzemyda, F Moreira & V Colla (eds), Information Systems and Technologies - WorldCIST 2023. Lecture Notes in Networks and Systems, vol. 800, Springer Science and Business Media Deutschland GmbH, pp. 85-91, 11th World Conference on Information Systems and Technologies, WorldCIST 2023, Pisa, Italy, 4/04/23. https://doi.org/10.1007/978-3-031-45645-9_8

Supervised Machine Learning Based Anomaly Detection in Online Social Networks. / Che, Chi Leng; Hwang, Ting Kai; Li, Yung Ming.
Information Systems and Technologies - WorldCIST 2023. ed. / Alvaro Rocha; Hojjat Adeli; Gintautas Dzemyda; Fernando Moreira; Valentina Colla. Springer Science and Business Media Deutschland GmbH, 2024. p. 85-91 (Lecture Notes in Networks and Systems; Vol. 800).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Supervised Machine Learning Based Anomaly Detection in Online Social Networks

AU - Che, Chi Leng

AU - Hwang, Ting Kai

AU - Li, Yung Ming

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - With the rapid development of online social networks (OSNs), a huge number of information provided by some entities around the world are well dispersed in OSNs every day. Most of those are useful but not all as anomalous entities utilize anomaly users to spread malicious content (like spam or rumors to achieve their pecuniary or political aims). In this paper, we propose a mechanism to detect such anomaly users according to the user profile and tweet content of each user. We design several features related to near-duplicate content (including lexical similarity and semantic similarity) to enhance the precision of detecting anomaly users. Utilizing the data by public honeypot dataset, the proposed approach deals with supervised learning approach to carry out the detection task.

AB - With the rapid development of online social networks (OSNs), a huge number of information provided by some entities around the world are well dispersed in OSNs every day. Most of those are useful but not all as anomalous entities utilize anomaly users to spread malicious content (like spam or rumors to achieve their pecuniary or political aims). In this paper, we propose a mechanism to detect such anomaly users according to the user profile and tweet content of each user. We design several features related to near-duplicate content (including lexical similarity and semantic similarity) to enhance the precision of detecting anomaly users. Utilizing the data by public honeypot dataset, the proposed approach deals with supervised learning approach to carry out the detection task.

KW - Anomaly Detection

KW - Near-Duplicate Computing

KW - Online Social Network

KW - Supervised Machine Learning

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DO - 10.1007/978-3-031-45645-9_8

M3 - Conference contribution

AN - SCOPUS:85186769015

SN - 9783031456442

T3 - Lecture Notes in Networks and Systems

SP - 85

EP - 91

BT - Information Systems and Technologies - WorldCIST 2023

A2 - Rocha, Alvaro

A2 - Adeli, Hojjat

A2 - Dzemyda, Gintautas

A2 - Moreira, Fernando

A2 - Colla, Valentina

PB - Springer Science and Business Media Deutschland GmbH

T2 - 11th World Conference on Information Systems and Technologies, WorldCIST 2023

Y2 - 4 April 2023 through 6 April 2023

ER -

Supervised Machine Learning Based Anomaly Detection in Online Social Networks

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