Abstract：Since there are many malicious users on the Internet, popular online websites sometimes have millions of registered users. The system cannot easily distinguish between fake accounts and legitimate users. Fake accounts registered by a single malicious user often have similar profiles. This paper presents a new framework to find fake accounts in large numbers of users. The framework uses username string patterns to classify the original data and then calculates the similarity as measured by the Levenshtein distance between any two elements in one category. Hierarchical clustering with a proper threshold then finds groups of fake accounts hidden in the large amount of registration data. Tests demonstrate the effectiveness of this framework which algorithm relies less on data dimensions and features than other algorithms.
Wang A H. Don't follow me:Spam detection in twitter[C]//Security and Cryptography (SECRYPT), Proceedings of the 2010 International Conference. Athens, Greece:IEEE Press, 2010:1-10.
[2]
Mohammad R M, Thabtah F, McCluskey L. Intelligent rule-based phishing websites classification[J]. IET Information Security, 2014, 8(3):153-160.
[3]
Marchal S, Saari K, Singh N, et al. Know your phish:Novel techniques for detecting phishing sites and their targets[C]//Distributed Computing Systems (ICDCS), 2016 IEEE 36th International Conference. Piscataway, NJ, USA:IEEE Press, 2016:323-333.
[4]
Malhotra A, Totti L, Meira Jr W, et al. Studying user footprints in different online social networks[C]//Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference. Piscataway, NJ, USA:IEEE Press, 2012:1065-1070.
[5]
Cao Q, Sirivianos M, Yang X W, et al. Aiding the detection of fake accounts in large scale social online services[C]//Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation. San Diego, CA, USA:USENIX Association, 2012:15-15.
[6]
Cao X, Freeman D M, Hwa T. Detecting clusters of fake accounts in online social networks[C]//Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security. Denver, CO, USA:ACM Press, 2015:91-101.
[7]
Fire M, Katz G, Elovici Y. Strangers intrusion detection-detecting spammers and fake profiles in social networks based on topology anomalies[J]. Human Journal, 2012, 1(1):26-39.
[8]
Jin L, Takabi H, Joshi J B D. Towards active detection of identity clone attacks on online social networks[C]//Proceedings of the first ACM conference on Data and application security and privacy. San Antonio, TX, USA:ACM, 2011:27-38.
[9]
CHENG Yang, QI Zhao. How to set and manage your network password:A multidimensional scheme of password reuse[C]//Conference on e-Business, e-Services and e-Society. Berlin:Springer, 2014:264-276.
[10]
Das A, Bonneau J, Caesar M, et al. The tangled web of password reuse[C]//Network and Distributed System Security Symposium. San Diego, CA, USA:The Internet Society, 2014:23-26.
[11]
Yang C, Hung J, Lin ZX. An analysis view on password patterns of Chinese Internet users[J]. Nankai Business Review International, 2013, 4(1):66-77.
[12]
LI Zhigong, HAN Weili, XU Wenyuan. A large-scale empirical analysis of Chinese Web passwords[C]//The 23rd USENIX Conference on Security Symposium. San Diego, CA, USA:USENIX Security, 2014:559-574.
2017, Vol. 57 
