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

    Research output: Contribution to journalArticlepeer-review


    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%
    Original languageEnglish
    Pages (from-to)340-356
    Number of pages17
    JournalBig Data Mining and Analytics
    Issue number2
    Publication statusPublished (VoR) - 23 Nov 2023


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


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