ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph
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 t...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020020 |
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| _version_ | 1832569348795924480 |
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| author | Muhammad Ajmal Azad Junaid Arshad Farhan Riaz |
| author_facet | Muhammad Ajmal Azad Junaid Arshad Farhan Riaz |
| author_sort | Muhammad Ajmal Azad |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-a2f2b451ad8f4a9e8775c05992f123d3 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a2f2b451ad8f4a9e8775c05992f123d32025-02-02T22:18:05ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-06-017234035610.26599/BDMA.2023.9020020ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity GraphMuhammad Ajmal Azad0Junaid Arshad1Farhan Riaz2College of Comnputer Science, Birmingham City University, Birmingham, B5 5JU, UKCollege of Comnputer Science, Birmingham City University, Birmingham, B5 5JU, UKSchool of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UKRobo 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%.https://www.sciopen.com/article/10.26599/BDMA.2023.9020020social network analysisreputationspam over internet technology (spit)unwanted callsrobo-callerstelephone network |
| spellingShingle | Muhammad Ajmal Azad Junaid Arshad Farhan Riaz ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph Big Data Mining and Analytics social network analysis reputation spam over internet technology (spit) unwanted calls robo-callers telephone network |
| title | ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph |
| title_full | ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph |
| title_fullStr | ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph |
| title_full_unstemmed | ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph |
| title_short | ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph |
| title_sort | robo spot detecting robocalls by understanding user engagement and connectivity graph |
| topic | social network analysis reputation spam over internet technology (spit) unwanted calls robo-callers telephone network |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020020 |
| work_keys_str_mv | AT muhammadajmalazad robospotdetectingrobocallsbyunderstandinguserengagementandconnectivitygraph AT junaidarshad robospotdetectingrobocallsbyunderstandinguserengagementandconnectivitygraph AT farhanriaz robospotdetectingrobocallsbyunderstandinguserengagementandconnectivitygraph |