Computer Science ›› 2018, Vol. 45 ›› Issue (7): 122-128.doi: 10.11896/j.issn.1002-137X.2018.07.020
• Information Security • Previous Articles Next Articles
LIU Xiao, WANG Xiao-guo
CLC Number:
[1]YU H F,KAMINSKY M,GIBBONS P B,et al.Sybilguard:Defending against sybil attacks via social networks[J].IEEE/ACM Transactions on Networking(TON),2008:16(3):576-589. [2]YU H,GIBBONS P B,KAMINSKY M,et al.Sybillimit:A nearo-ptimal social network defense against sybil attacks[J].IEEE/ACM Transactions on Networking,2010,18(3):885-898. [3]DANEZIS G,MITTAL P.Sybilinfer:Detecting sybil nodesusing social networks[C]∥Proceedings of the Network and Distributed System Security Symposium,NDSS.San Diego,California,USA,2009. [4]NGUYEN T,JINYANG L,LAKSHMINARAYANAN S,et al.Optimal sybil-resilient node admission control[C]∥Proceedings of IEEE INFOCOM.Shanghai,China,2011. [5]WEI W,FENGYUANF X,CHIU C T,et al.Sybildefender:Adefense mechanism for sybil attacks in large social networks[J].IEEE Transactions on Parallel and Distributed Systems,2013,24(12):2492-2502. [6]LU S,SHUCHENG Y,WENJING L,et al.Sybilshield:Anagent-aided social network-based sybil defense among multiple communities[C]∥Proceedings IEEE INFOCOM.Turin,Italy,2013. [7]CAO Q,SIRIVIANOS M,YANG X,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(NSDI’12).2012:15. [8]ZOLTA G,HECTOR G M,JAN P.Combating web spam with trustrank[C]∥Proceedings of the 30th International Conference on Very Large Databases.2004. [9]YANG C,HARKREADER R,ZHANG J,et al.Analyzing spam-mers’ social networks for fun and profit:A case study of cyber criminal ecosystem on twitter[C]∥Proceedings of the 21st International Conference on World Wide Web.New York,NY,USA:ACM,2012:71-80. [10]KOLDA T G,PROCOPIO M J.Generalized badrank with gra-duated trust[R].Sandia National Laboratories,2009. [11]LESNIEWSKILAAS C,KAASHOEKM F.Whanau:A sybil-proof distributed hash table[C]∥Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation.Berkeley,CA,USA:USENIX Association,2010:8. [12]MOHAISEN A,YUN A,KIM Y.Measuring the mixing time of social graphs[C]∥Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement.New York,NY,USA:ACM,2010:383-389. [13]BEHRENDS E.Introduction to markov chains:with special em-phasis on rapid mixing[M]∥Advanced Lectures in Mathema-tics,2000. [14]BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008,2008(10):155-168. [15]PANDIT S,CHAU D H,WANG S,et al.Netprobe:A fast and scalable system for fraud detection in online auction networks[C]∥Proceedings of the 16th International Conference on World Wide Web.New York,NY,USA:ACM,2007:201-210. [16]SHEBUTI R,LEMAN A.Collective Opinion Spam Detection:Bridging Review Networks and Metadata[C]∥Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’15).ACM,New York,NY,USA,2015:985-994. [17]PEARL J.Probabilistic reasoning in intelligent systems:Networks of plausible inference[M].San Francisco:Morgan Kaufmann Publishers Inc.,1988. [18]JIA J,WANG B,ZHANG L,et al.AttriInfer:Inferring User Attributes in Online Social Networks Using Markov Random Fields[C]∥International Conference on World Wide Web.2017:1561-1569. [19]GATTERBAUER W,GUNNEMANN S,KOUTRA D,et al.Linearized and single-pass belief propagation[J].Proceedings of the Vidb Endowment,2014,8(5):581-592. [20]WANG B,GONG N Z,FU H.Gang:Detecting fraudulent users in online social networks via guilt-by-association on directed graphs[C]∥2017 IEEE International Conference on Data Mi-ning (ICDM).New Orleans,LA,USA,2017:465-474. [21]SAAD Y.Iterative methods for sparse linear systems(2nd ed)[M].Reading,MA:Society for Industrial and Applied Mathematics,2003. [22]HOLME P,KIM B J.Growing scale-free networks with tunable clustering[J].Physical Review E,2002,65(22):026107. [23]BAHNSEN A C,AOUADA D,STOJANOVIC A.Feature engineering strategies for credit card fraud detection[J].Expert Systems with Applications An International Journal,2016,51(C):134-142. |
[1] | WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25. |
[2] | LI Rong-fan, ZHONG Ting, WU Jin, ZHOU Fan, KUANG Ping. Spatio-Temporal Attention-based Kriging for Land Deformation Data Interpolation [J]. Computer Science, 2022, 49(8): 33-39. |
[3] | HOU Xia-ye, CHEN Hai-yan, ZHANG Bing, YUAN Li-gang, JIA Yi-zhen. Active Metric Learning Based on Support Vector Machines [J]. Computer Science, 2022, 49(6A): 113-118. |
[4] | WANG Yu-fei, CHEN Wen. Tri-training Algorithm Based on DECORATE Ensemble Learning and Credibility Assessment [J]. Computer Science, 2022, 49(6): 127-133. |
[5] | YAO Xiao-ming, DING Shi-chang, ZHAO Tao, HUANG Hong, LUO Jar-der, FU Xiao-ming. Big Data-driven Based Socioeconomic Status Analysis:A Survey [J]. Computer Science, 2022, 49(4): 80-87. |
[6] | XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo. Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [J]. Computer Science, 2022, 49(3): 288-293. |
[7] | KONG Yu-ting, TAN Fu-xiang, ZHAO Xin, ZHANG Zheng-hang, BAI Lu, QIAN Yu-rong. Review of K-means Algorithm Optimization Based on Differential Privacy [J]. Computer Science, 2022, 49(2): 162-173. |
[8] | MA Dong, LI Xin-yuan, CHEN Hong-mei, XIAO Qing. Mining Spatial co-location Patterns with Star High Influence [J]. Computer Science, 2022, 49(1): 166-174. |
[9] | ZHANG Ya-di, SUN Yue, LIU Feng, ZHU Er-zhou. Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index [J]. Computer Science, 2022, 49(1): 121-132. |
[10] | XU Hui-hui, YAN Hua. Relative Risk Degree Based Risk Factor Analysis Algorithm for Congenital Heart Disease in Children [J]. Computer Science, 2021, 48(6): 210-214. |
[11] | ZHANG Yan-jin, BAI Liang. Fast Symbolic Data Clustering Algorithm Based on Symbolic Relation Graph [J]. Computer Science, 2021, 48(4): 111-116. |
[12] | ZHANG Han-shuo, YANG Dong-ju. Technology Data Analysis Algorithm Based on Relational Graph [J]. Computer Science, 2021, 48(3): 174-179. |
[13] | ZOU Cheng-ming, CHEN De. Unsupervised Anomaly Detection Method for High-dimensional Big Data Analysis [J]. Computer Science, 2021, 48(2): 121-127. |
[14] | LIU Xin-bin, WANG Li-zhen, ZHOU Li-hua. MLCPM-UC:A Multi-level Co-location Pattern Mining Algorithm Based on Uniform Coefficient of Pattern Instance Distribution [J]. Computer Science, 2021, 48(11): 208-218. |
[15] | LIU Xiao-nan, SONG Hui-chao, WANG Hong, JIANG Duo, AN Jia-le. Survey on Improvement and Application of Grover Algorithm [J]. Computer Science, 2021, 48(10): 315-323. |
|