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

Azad, Muhammad Ajmal and Arshad, Junaid and Farhan, Riaz (2023) ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph. Big Data Mining and Analytics. (In Press)

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Robo or unsolicited calls have become a persistent is-sue in telecommunication networks, posing significant challenges to individuals, businesses, and regulatory authorities. These calls not only trick users to disclose 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 used a large anonymized data set (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%

Item Type: Article
1 August 2023Accepted
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems
CAH11 - computing > CAH11-01 - computing > CAH11-01-08 - others in computing
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Muhammadajmal Azad
Date Deposited: 04 Sep 2023 11:37
Last Modified: 04 Sep 2023 11:37

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