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

Muhammad Ajmal Azad; Junaid Arshad; Farhan Riaz

doi:10.26599/BDMA.2023.9020020

Big Data Mining and Analytics 2024, 7(2): 340-356 https://doi.org/10.26599/BDMA.2023.9020020

Open Access | Issue | Published: 22 April 2024

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

Show Author's Information Hide Author's Information Muhammad Ajmal Azad^¹(

), Junaid Arshad^¹, Farhan Riaz^²

1College of Comnputer Science, Birmingham City University, Birmingham, B5 5JU, UK

2School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK

Keywords:

social network analysis, reputation, unwanted calls, robo-callers, telephone network, Spam Over Internet Technology (SPIT)

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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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%.

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

Show Author's information Hide Author's Information Muhammad Ajmal Azad^¹(

), Junaid Arshad^¹, Farhan Riaz^²

1College of Comnputer Science, Birmingham City University, Birmingham, B5 5JU, UK

2School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK

Abstract

Keywords: social network analysis, reputation, unwanted calls, robo-callers, telephone network, Spam Over Internet Technology (SPIT)

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Received: 28 April 2023

Revised: 01 July 2023

Accepted: 25 July 2023

Published: 22 April 2024

Issue date: June 2024

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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/).