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POISED: Spotting Twitter Spam Off the Beaten Paths

Authors:

Shirin Nilizadeh,

Francois Labrèche,

Alireza Sedighian,

José Fernandez,

Christopher Kruegel,

Gianluca Stringhini,

Giovanni VignaAuthors Info & Claims

CCS '17: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

Pages 1159 - 1174

https://doi.org/10.1145/3133956.3134055

Published: 30 October 2017 Publication History

Abstract

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses.

Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns.

In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks.

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Cited By

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https://dl.acm.org/doi/10.1145/3613904.3642787
Alsoubai APark JQadir SStringhini GRazi AWisniewski P(2024)Systemization of Knowledge (SoK): Creating a Research Agenda for Human-Centered Real-Time Risk Detection on Social Media PlatformsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642315(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642315
Diboune ASlimani HNacer HBeghdad Bey K(2024)A comprehensive survey on community detection methods and applications in complex information networksSocial Network Analysis and Mining10.1007/s13278-024-01246-514:1Online publication date: 18-Apr-2024
https://doi.org/10.1007/s13278-024-01246-5
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Index Terms

POISED: Spotting Twitter Spam Off the Beaten Paths
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
  2. Software and application security
    1. Social network security and privacy

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cover image ACM Conferences

CCS '17: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

October 2017

2682 pages

ISBN:9781450349468

DOI:10.1145/3133956

General Chair:
Bhavani Thuraisingham
The University of Texas at Dallas, USA
,
Program Chairs:
David Evans
University of Virginia
,
Tal Malkin
Columbia University
,
Dongyan Xu
Purdue University

Copyright © 2017 ACM.

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Published: 30 October 2017

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CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security

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CCS '17 Paper Acceptance Rate 151 of 836 submissions, 18%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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https://dl.acm.org/doi/10.1145/3613904.3642787
Alsoubai APark JQadir SStringhini GRazi AWisniewski P(2024)Systemization of Knowledge (SoK): Creating a Research Agenda for Human-Centered Real-Time Risk Detection on Social Media PlatformsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642315(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642315
Diboune ASlimani HNacer HBeghdad Bey K(2024)A comprehensive survey on community detection methods and applications in complex information networksSocial Network Analysis and Mining10.1007/s13278-024-01246-514:1Online publication date: 18-Apr-2024
https://doi.org/10.1007/s13278-024-01246-5
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