Chinese Journal of Network and Information Security ›› 2018, Vol. 4 ›› Issue (3): 13-23.doi: 10.11959/j.issn.2096-109x.2018025
• Comprehensive Reviews • Previous Articles Next Articles
Qiang QU(),Hongtao YU,Ruiyang HUANG
Revised:
2018-02-27
Online:
2018-03-15
Published:
2018-04-09
Supported by:
CLC Number:
Qiang QU, Hongtao YU, Ruiyang HUANG. Research progress of abnormal user detection technology in social network[J]. Chinese Journal of Network and Information Security, 2018, 4(3): 13-23.
"
检测特征 | 特点 | 关键 | 特征评估 |
属性特征 | 采用人为设计方法,容易被攻击者绕过,算法设计简单,效率低,准确率相对较低,数据量级小,具有严格隐私保护 | 突破隐私保护 | 不常用 |
内容特征 | 采用自然语言处理方式,容易被攻击者绕过,算法设计复杂,效率低,准确率相对较低,数据量级大,具有轻微隐私保护 | 设计复杂算法,合理语言模式 | 常用 |
网络特征 | 采用复杂网络处理方式,不易被攻击者绕过,算法设计简单,效率低,准确率相对较低,数据量级大,不具有隐私保护 | 掌握全局结构 | 主流 |
活动特征 | 采用行为模式分析处理方式,不易被攻击者绕过,算法设计简单,效率高,准确率高,数据量级大,具有轻微隐私保护 | 选取区分度最大的活动信息 | 主流 |
辅助特征 | 采用时间序列模型分析,不易被攻击者绕过,算法设计复杂,效率高,准确率高,数据量级小,具有轻微隐私保护 | 有效利用时间维度信息 | 热门 |
[1] | RAYANA S , AKOGLU L . Collective opinion spam detection:Bridging review networks and metadata[C]// The 21st ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. 2015: 985-994. |
[2] | LIM E P , NGUYEN V A , JINDAL N ,et al. Detecting product review spammers using rating behaviors[C]// The 19th ACM International Conference on Information and Knowledge Management. 2010: 939-948. |
[3] | MALBON J . Taking fake online consumer reviews seriously[J]. Journal of Consumer Policy, 2013,36(2): 139-157. |
[4] | CAO Q , SIRIVIANOS M , YANG X ,et al. Aiding the detection of fake accounts in large scale social online services[C]// The 9th Usenix Conference on Networked Systems Design and Implementation. 2012: 15-15. |
[5] | CHENG J , BERNSTEIN M , DANESCU-NICULESCU-MIZIL C ,et al. Anyone can become a troll:causes of trolling behavior in online discussions[C]// ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017). 2017. |
[6] | HINDUJA S , PATCHIN J W . Bullying,cyberbullying,and suicide[J]. Archives of Suicide Research, 2010,14(3): 206-221. |
[7] | ZAFARANI R , LIU H . 10 bits of surprise:Detecting malicious users with minimum information[C]// The 24th ACM International on Conference on Information and Knowledge Management. 2015: 423-431. |
[8] | KUMAR S , CHENG J , LESKOVEC J . Antisocial behavior on the Web:characterization and detection[C]// The 26th International Conference on World Wide Web Companion. 2017: 947-950. |
[9] | JIANG M , KUMAR S , SUBRAHMANIAN V S ,et al. Data-driven approaches towards malicious behavior modeling[C]// The 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 2017, 19:42. |
[10] | JIANG M , CUI P , FALOUTSOS C . Suspicious behavior detection:current trends and future directions[J]. IEEE Intelligent Systems, 2016,31(1): 31-39. |
[11] | BEUTEL A , AKOGLU L , FALOUTSOS C . Graph-based user behavior modeling:from prediction to fraud detection[C]// The 21st ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. 2015: 2309-2310. |
[12] | YE J , AKOGLU L . Discovering opinion spammer groups by network footprints[C]// Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2015: 267-282. |
[13] | PRAKASH B A , SRIDHARAN A , SESHADRI M ,et al. Eigenspokes:surprising patterns and scalable community chipping in large graphs[C]// Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2010: 435-448. |
[14] | HOOI B , SONG H A , BEUTEL A ,et al. Fraudar:bounding graph fraud in the face of camouflage[C]// The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 895-904. |
[15] | JIANG M , CUI P , BEUTEL A ,et al. Inferring strange behavior from connectivity pattern in social networks[C]// Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2014: 126-138. |
[16] | 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. |
[17] | YAMAK Z , SAUNIER J , VERCOUTER L . Detection of multiple identity manipulation in collaborative projects[C]// The 25th International Conference Companion on World Wide Web. 2016: 955-960. |
[18] | WU S , LIU Q , LIU Y ,et al. Information credibility evaluation on social media[C]// The 30th AAAI Conference on Artificial Intelligence. 2016: 4403-4404. |
[19] | FRIGGERI A , ADAMIC L A , ECKLES D ,et al. Rumor cascades[J]. Dalton Transactions, 2014,43(16): 6108-19. |
[20] | HOSSEINMARDI H , GHASEMIANLANGROODI A , HAN R ,et al. Towards understanding cyberbullying behavior in a semianonymous social network[C]// 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2014: 244-252. |
[21] | ADLER B , DE ALFARO L , PYE I . Detecting wikipedia vandalism using wikitrust[J]. Notebook Papers of CLEF, 2010,1: 22-23. |
[22] | GUPTA A , LAMBA H , KUMARAGURU P ,et al. Faking sandy:characterizing and identifying fake images on twitter during hurricane sandy[C]// The 22nd International Conference on World Wide Web. 2013: 729-736. |
[23] | HU X , TANG J , ZHANG Y ,et al. Social spammer detection in microblogging[C]// The International Joint Conference on Artificial Intelligence. 2013: 2633-2639. |
[24] | HU X , TANG J , GAO H ,et al. Social spammer detection with sentiment information[C]// 2014 IEEE International Conference on Data Mining (ICDM). 2014: 180-189. |
[25] | LEE K , CAVERLEE J , WEBB S . Uncovering social spammers:social honeypots+ machine learning[C]// The 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2010: 435-442. |
[26] | BEUTEL A , XU W , GURUSWAMI V ,et al. Copycatch:stopping group attacks by spotting lockstep behavior in social networks[C]// The 22nd International Conference on World Wide Web. 2013: 119-130. |
[27] | LI Y , MARTINEZ O , CHEN X ,et al. In a world that counts:clustering and detecting fake social engagement at scale[C]// The 25th International Conference on World Wide Web. 2016: 111-120. |
[28] | WU L , HU X , MORSTATTER F ,et al. Adaptive spammer detection with sparse group modeling[C]// The International AAAI Conference on Web and Social Media. 2017: 319-326. |
[29] | XU C , SU B , CHENG Y ,et al. An adaptive fusion algorithm for spam detection[J]. IEEE Intelligent Systems, 2014,29(4): 2-8. |
[30] | JIANG M , CUI P , BEUTEL A ,et al. Catchsync:catching synchronized behavior in large directed graphs[C]// The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014: 941-950. |
[31] | RATKIEWICZ J , CONOVER M , MEISS M R ,et al. Detecting and tracking political abuse in social media[C]// The International Conference on Weblogs and Social Media(ICWSM). 2011: 297-304. |
[32] | TSIKERDEKIS M , ZEADALLY S . Multiple account identity deception detection in social media using nonverbal behavior[J]. IEEE Transactions on Information Forensics and Security, 2014,9(8): 1311-1321. |
[33] | AKOGLU L , MCGLOHON M , FALOUTSOS C . Oddball:spotting anomalies in weighted graphs[C]// Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2010: 410-421. |
[34] | HU X , TANG J , LIU H . Online social spammer detection[C]// The 28th AAAI Conference on Artificial Intelligence. 2014: 59-65. |
[35] | HORNE B D , ADALI S . This just in:fake news packs a lot in title,uses simpler,repetitive content in text body,more similar to satire than real news[C]// The 2nd International Workshop on News and Public Opinion. 2017. |
[36] | KUMAR S , SPEZZANO F , SUBRAHMANIAN V S . Vews:a wikipedia vandal early warning system[C]// The 21sh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: 607-616. |
[37] | SHIN K , HOOI B , KIM J ,et al. D-cube:dense-block detection in terabyte-scale tensors[C]// The 10th ACM International Conference on Web Search and Data Mining. 2017: 681-689. |
[38] | KUMAR S , WEST R , LESKOVEC J . Disinformation on the web:Impact,characteristics,and detection of wikipedia hoaxes[C]// The 25th International Conference on World Wide Web. 2016: 591-602. |
[39] | RATKIEWICZ J , CONOVER M , MEISS M ,et al. Truthy:mapping the spread of astroturf in microblog streams[C]// The 20th International Conference Companion on World Wide Web. 2011: 249-252. |
[40] | GIATSOGLOU M , CHATZAKOU D , SHAH N ,et al. Nd-sync:detecting synchronized fraud activities[C]// Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2015: 201-214. |
[41] | SUBRAHMANIAN V S , AZARIA A , DURST S ,et al. The DARPA Twitter bot challenge[J]. Computer, 2016,49(6): 38-46. |
[42] | PEREZ C , LEMERCIER M , BIRREGAH B ,et al. Spot 1.0:scoring suspicious profiles on twitter[C]// The International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2011: 377-381. |
[43] | ADLER B T , DE ALFARO L,MOLA-VELASCO S M , et al . Wikipedia vandalism detection:combining natural language,metadata,and reputation features[C]// The International Conference on Intelligent Text Processing and Computational Linguistics. 2011: 277-288. |
[44] | CHENG J , DANESCU-NICULESCU-MIZIL C , LESKOVEC J . Antisocial behavior in online discussion communities[J].Computer Science,2015. Computer Science, 2015. |
[45] | KUMAR S , CHENG J , LESKOVEC J ,et al. An army of me:Sockpuppets in online discussion communities[C]// The 26th International Conference on World Wide Web. 2017: 857-866. |
[46] | DICKERSON J P , KAGAN V , SUBRAHMANIAN V S . Using sentiment to detect bots on twitter:are humans more opinionated than bots?[C]// 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2014: 620-627. |
[47] | MUKHERJEE A , VENKATARAMAN V , LIU B ,et al. What yelp fake review filter might be doing?[C]// The International Conference on Web and Social Media(ICWSM). 2013. |
[48] | SOLORIO T , HASAN R , MIZAN M . A case study of sockpuppet detection in wikipedia[C]// The Workshop on Language Analysis in Social Media. 2013: 59-68. |
[49] | GANI K , HACID H , SKRABA R . Towards multiple identity detection in social networks[C]// The 21st International Conference on World Wide Web. 2012: 503-504. |
[50] | GAO H , HU J , WILSON C ,et al. Detecting and characterizing social spam campaigns[C]// The 10th ACM Sigcomm Conference on Internet Measurement. 2010: 35-47. |
[51] | BENEVENUTO F , MAGNO G , RODRIGUES T ,et al. Detecting spammers on twitter[C]// Collaboration,Electronic Messaging,Anti-abuse and Spam Conference (CEAS). 2010:12. |
[52] | SHAH N , BEUTEL A , GALLAGHER B ,et al. Spotting suspicious link behavior with fbox:an adversarial perspective[C]// 2014 IEEE International Conference on Data Mining (ICDM). 2014: 959-964. |
[53] | KUNEGIS J , LOMMATZSCH A , BAUCKHAGE C . The slashdot zoo:mining a social network with negative edges[C]// The 18th International Conference on World Wide Web. 2009: 741-750. |
[54] | XU Q , XIANG E W , YANG Q ,et al. SMS spam detection using noncontent features[J]. IEEE Intelligent Systems, 2012,27(6): 44-51. |
[55] | KUMAR S , SPEZZANO F , SUBRAHMANIAN V S . Accurately detecting trolls in slashdot zoo via decluttering[C]// 2014 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2014: 188-195. |
[56] | SHACHAF P , HARA N . Beyond vandalism:Wikipedia trolls[J]. Journal of Information Science, 2010,36(3): 357-370. |
[57] | CHENG J , DANESCU-NICULESCU-MIZIL C , LESKOVEC J . How community feedback shapes user behavior[C]// The International Conference on Weblogs and Social Media (ICWSM). 2014. |
[58] | JINDAL N , LIU B . Opinion spam and analysis[C]// The 2008 International Conference on Web Search and Data Mining. 2008: 219-230. |
[59] | MUKHERJEE A , LIU B , GLANCE N . Spotting fake reviewer groups in consumer reviews[C]// The 21st International Conference on World Wide Web. 2012: 191-200. |
[60] | HOOI B , SHAH N , BEUTEL A ,et al. Birdnest:bayesian inference for ratings-fraud detection[C]// 2016 SIAM International Conference on Data Mining.Society for Industrial and Applied Mathematics. 2016: 495-503. |
[61] | FEI G , MUKHERJEE A , LIU B ,et al. Exploiting burstiness in reviews for review spammer detection[C]// The 7th International AAAI Conference on Weblogs and Social Media (ICWSM). 2013: 175-184. |
[62] | BU Z , XIA Z , WANG J . A sock puppet detection algorithm on virtual spaces[J]. Knowledge-Based Systems, 2013,37: 366-377. |
[63] | JIANG M , CUI P , WANG F ,et al. Fema:flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery[C]// The 20th ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. 2014: 1186-1195. |
[64] | YU H , KAMINSKY M , GIBBONS P B ,et al. Sybilguard:defending against sybil attacks via social networks[C]// ACM Sigcomm Computer Communication Review. 2006: 267-278. |
[65] | YU H , GIBBONS P B , KAMINSKY M ,et al. Sybillimit:a near-optimal social network defense against sybil attacks[C]// IEEE Symposium on Security and Privacy. 2008: 3-17. |
[66] | WEI W , XU F , TAN C C ,et al. Sybildefender:Defend against sybil attacks in large social networks[C]// IEEE INFOCOM. 2012: 1951-1959. |
[67] | GONG N Z , FRANK M , MITTAL P . Sybilbelief:a semi-supervised learning approach for structure-based sybil detection[J]. IEEE Transactions on Information Forensics and Security, 2014,9(6): 976-987. |
[1] | Wenqi SHI, Xiangyang LUO, Jiashan GUO. Social network user geolocating method based on weighted least squares [J]. Chinese Journal of Network and Information Security, 2022, 8(3): 41-52. |
[2] | Rongna XIE, Xiaonan FAN, Lin YUAN, Zichen GUO, Jiayu ZHU, Guozhen SHI. Research on extended access control mechanism in online social network [J]. Chinese Journal of Network and Information Security, 2021, 7(5): 123-131. |
[3] | Shuo WANG, Jun BAI, Bailing WANG, Xu ZHANG, Hongri LIU. Accelerated traffic replay method based on time compression [J]. Chinese Journal of Network and Information Security, 2021, 7(5): 178-188. |
[4] | Hao ZHAO, Hui SHU, Fei KANG, Ying XING. High resistance botnet based on smart contract [J]. Chinese Journal of Network and Information Security, 2021, 7(4): 30-41. |
[5] | Zhongyuan JIANG, Xianyu CHEN, Jianfeng MA. Survey of community privacy in social network [J]. Chinese Journal of Network and Information Security, 2021, 7(2): 10-21. |
[6] | Xin ZHANG,Weizhong QIANG,Yueming WU,Deqing ZOU,Hai JIN. Mining behavior pattern of mobile malware with convolutional neural network [J]. Chinese Journal of Network and Information Security, 2020, 6(6): 35-44. |
[7] | Luhui YANG,Huiwen BAI,Guangjie LIU,Yuewei DAI. Lightweight malicious domain name detection model based on separable convolution [J]. Chinese Journal of Network and Information Security, 2020, 6(6): 112-120. |
[8] | Pei WANG,Yan JIA,Aiping LI,Qianyue JIANG. De-anonymiation method for networks based on DeepLink [J]. Chinese Journal of Network and Information Security, 2020, 6(4): 104-108. |
[9] | Qiang QU,Hongtao YU,Ruiyang HUANG. Attention-based approach of detecting spam in social networks [J]. Chinese Journal of Network and Information Security, 2020, 6(1): 54-61. |
[10] | JIA Chunfu,LI Ruiqi,TIAN Meiqi,CHENG Xiaoyang. Discuss on cultivating mode of information security and law inter-disciplinary talents [J]. Chinese Journal of Network and Information Security, 2019, 5(3): 31-35. |
[11] | CHEN Xingshu,WANG Haizhou,WANG Wenxian,YANG Ping,RUAN Shuhua. Exploring the talent training mode of“cybersecurity doctor” [J]. Chinese Journal of Network and Information Security, 2019, 5(3): 36-43. |
[12] | WENG Jian,WEI Linfeng,ZHANG Yue. Discussion on the cultivation of cyber security talents [J]. Chinese Journal of Network and Information Security, 2019, 5(3): 44-53. |
[13] | CHEN Wei,YIN Zhenqiang,HAN Zhengfu,YU Nenghai. Quantum information course for the undergraduate students of cyber security [J]. Chinese Journal of Network and Information Security, 2019, 5(3): 81-88. |
[14] | Lixun LI,Bin ZHANG,Shuqin DONG. Host security threat analysis approach for network dynamic defense [J]. Chinese Journal of Network and Information Security, 2018, 4(4): 48-55. |
[15] | Dequan YANG,Weimin LIU,Zhou YU. Research on active defense application based on honeypot [J]. Chinese Journal of Network and Information Security, 2018, 4(1): 57-62. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|