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

作     者：Muhammad Ajmal Azad Junaid Arshad Farhan Riaz 

作者机构：College of Comnputer ScienceBirmingham City UniversityBirminghamB55JUUK School of Computer ScienceUniversity of LincolnLincolnLN67TSUK 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2024年第7卷第2期

页      面：340-356页

核心收录：

学科分类：050301[文学-新闻学] 05[文学] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0503[文学-新闻传播学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

摘      要：Robo or unsolicited calls have become a persistent issue in telecommunication networks,posing significant challenges to individuals,businesses,and regulatory *** 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 ***,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 *** trust score of the callers is then used along with machine learning models to classify them as legitimate or *** use a large anonymized dataset(call detailed records)from a large telecommunication provider containing more than 1 billion records collected over 10 *** have conducted extensive evaluation demonstrating that the proposed approach achieves high accuracy and detection rate whilst minimizing the error ***,the proposed features when used collectively achieve a true-positive rate of around 97%with a false-positive rate of less than 0.01%.