Improving Fraudulent Profile Detection with Machine Learning and Negative Selection

Ahmed Slimani; Chahreddine Medjahed; Freha Mezzoudj; Abdellatif Rahmoun; Narimane Wafaa Krolkral; Meriem Bahadj

doi:10.2991/978-94-6463-805-9_10

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Improving Fraudulent Profile Detection with Machine Learning and Negative Selection

Authors

Ahmed Slimani¹^{, 5}^{, *}, Chahreddine Medjahed², Freha Mezzoudj³, Abdellatif Rahmoun⁴, Narimane Wafaa Krolkral², Meriem Bahadj⁵

¹LabRI-SBA Lab, Ahmed Draia University – ADRAR, Adrar, Algeria

²Hassiba benbouali chlef, University Algeria, Algiers, Algeria

³The National Polytechnic School of Oran Algeria, Es Sénia, Algeria

⁴LabRI-SBA Lab, The Higher School of Computer Science of Sidi Bel Abbess, Sidi Bel Abbès, Algeria

⁵Ahmed Draia University – ADRAR, Adrar, Algeria

^*Corresponding author. Email: ah.slimani@esi-sba.dz

Corresponding Author

Ahmed Slimani

Available Online 5 August 2025.

DOI: 10.2991/978-94-6463-805-9_10 How to use a DOI?
Keywords: Fake Profiles; Social Networks; Machine Learning (ML); Negative Selection; Profile Detection Artificial Immune System
Abstract: The rise of social networks has increased user interactions but also led to a surge in fake accounts, threatening privacy and security. These fraudulent profiles undermine trust and facilitate misinformation, making their detection crucial. This study proposes an improved machine learning approach using the Negative Selection Algorithm (NSA) to enhance fake account identification. Inspired by the human immune system, NSA helps distinguish between genuine and fraudulent accounts. Additionally, K-Means clustering refines the training dataset by removing anomalies, improving classification accuracy. Experimental results show promising improvements, contributing to more effective fake account detection and a safer online environment.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 5 August 2025
ISBN: 978-94-6463-805-9
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-805-9_10 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Ahmed Slimani
AU  - Chahreddine Medjahed
AU  - Freha Mezzoudj
AU  - Abdellatif Rahmoun
AU  - Narimane Wafaa Krolkral
AU  - Meriem Bahadj
PY  - 2025
DA  - 2025/08/05
TI  - Improving Fraudulent Profile Detection with Machine Learning and Negative Selection
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 76
EP  - 84
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_10
DO  - 10.2991/978-94-6463-805-9_10
ID  - Slimani2025
ER  -

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