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Open Access

ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph

College of Comnputer Science, Birmingham City University, Birmingham, B5 5JU, UK
School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK
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Abstract

Robo or unsolicited calls have become a persistent issue in telecommunication networks, posing significant challenges to individuals, businesses, and regulatory authorities. These calls not only trick users into disclosing their private and financial information, but also affect their productivity through unwanted phone ringing. A proactive approach to identify and block such unsolicited calls is essential to protect users and service providers from potential harm. Therein, this paper proposes a solution to identify robo-callers in the telephony network utilising a set of novel features to evaluate the trustworthiness of callers in a network. The trust score of the callers is then used along with machine learning models to classify them as legitimate or robo-caller. We use a large anonymized dataset (call detailed records) from a large telecommunication provider containing more than 1 billion records collected over 10 days. We have conducted extensive evaluation demonstrating that the proposed approach achieves high accuracy and detection rate whilst minimizing the error rate. Specifically, the proposed features when used collectively achieve a true-positive rate of around 97% with a false-positive rate of less than 0.01%.

Keywords

social network analysisreputationSpam Over Internet Technology (SPIT)unwanted callsrobo-callerstelephone network

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Big Data Mining and Analytics
Volume 7 Issue 2,
June 2024
Pages 340-356
DOI: 10.26599/BDMA.2023.9020020
Cite this article:
Azad MA, Arshad J, Riaz F. ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph. Big Data Mining and Analytics, 2024, 7(2): 340-356. https://doi.org/10.26599/BDMA.2023.9020020

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Received: 28 April 2023
Revised: 01 July 2023
Accepted: 25 July 2023
Published: 22 April 2024
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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