DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection

作     者：Yuanyuan Ma Ang Chen Cunzhi Hou Ruixia Jin Jinghui Zhang Ruixiang Li 

作者机构：College of Computer and Information EngineeringHenan Normal UniversityXinxiang453007China Intelligent Medical EngineeringSanQuan Medical CollegeXinxiang453003China Information Engineering UniversityInformation Engineering UniversityZhengzhou450001China Key Laboratory of Cyberspace Situation Awareness of Henan ProvinceZhengzhou450001China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第11期

页      面：3301-3323页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the National Natural Science Foundation of China under Grant No.62002103 Henan Province Science Foundation for Youths No.222300420058 Henan Province Science and Technology Research Project No.232102321064 Teacher Education Curriculum Reform Research Priority Project No.2023-JSJYZD-011 

主　　题：Fraudulent IP identification homology detection clustering genetic optimization algorithm telecom fraud identification 

摘      要：Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer ***,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change *** address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP ***,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent ***,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent ***,the risk value of the target IP is calculated and the IP is identified based on the risk value *** results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent ***,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud *** results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.