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

作　　者：Muhammad Ajmal Azad Junaid Arshad Farhan Riaz 

机构地区：[1]College of Comnputer Science,Birmingham City University,Birmingham,B55JU,UK [2]School of Computer Science,University of Lincoln,Lincoln,LN67TS,UK

出　　处：《Big Data Mining and Analytics》2024年第2期340-356,共17页大数据挖掘与分析（英文）

摘　　要：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%.

关 键 词：social network analysis REPUTATION unwanted calls robo-callers telephone network Spam Over Internet Technology(SPIT) 

分 类 号：G210.7[文化科学—新闻学] TP391.1[自动化与计算机技术—计算机应用技术]