Novel metrics and LSH algorithms for unsupervised, real-time anomaly detection in multi-aspect data streams
Given a vast online stream of transactions in e-markets, how can we detect fraudulent traders and suspicious behaviors in an unsupervised manner? Can we detect them in constant time and memory? Fraud detection in e-markets is increasingly challenging due to the scale and complexity of multi-aspect d...
Uloženo v:
| Vydáno v: | Engineering science and technology, an international journal Ročník 69; s. 102119 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.09.2025
Elsevier |
| Témata: | |
| ISSN: | 2215-0986, 2215-0986 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Given a vast online stream of transactions in e-markets, how can we detect fraudulent traders and suspicious behaviors in an unsupervised manner? Can we detect them in constant time and memory? Fraud detection in e-markets is increasingly challenging due to the scale and complexity of multi-aspect data streams. This study introduces SATrade, an unsupervised and scalable approach for real-time anomaly detection in big multi-aspect data streams. This approach proposes two novel Locality-Sensitive Hashing (LSH) functions: Gaussian projections to preserve numerical distances and collision-resistant linear hashing to prevent the increase in dimensionality of the categorical data. The main contributions include the Collusiveness metric, which detects group anomalies through statistical divergence analysis, and the RR-ISF, which prioritizes rare burst patterns. An exponential decay mechanism (λ) ensures adaptability to evolving fraud tactics without retraining, while PCA handles feature correlation. In extensive experiments on five real datasets, using both synthetic and real labels, SATrade achieved 99 % AUC, 93 % F-measure, and 0.2 ms/record latency, which is a significant improvement over the six baseline methods. The framework’s interpretability allows tracing anomalies to fraudulent behaviors like sudden order spikes. The constant memory consumption of 0.25 MB per record and linear scalability make SATrade suitable for high-frequency environments and online platforms. |
|---|---|
| AbstractList | Given a vast online stream of transactions in e-markets, how can we detect fraudulent traders and suspicious behaviors in an unsupervised manner? Can we detect them in constant time and memory? Fraud detection in e-markets is increasingly challenging due to the scale and complexity of multi-aspect data streams. This study introduces SATrade, an unsupervised and scalable approach for real-time anomaly detection in big multi-aspect data streams. This approach proposes two novel Locality-Sensitive Hashing (LSH) functions: Gaussian projections to preserve numerical distances and collision-resistant linear hashing to prevent the increase in dimensionality of the categorical data. The main contributions include the Collusiveness metric, which detects group anomalies through statistical divergence analysis, and the RR-ISF, which prioritizes rare burst patterns. An exponential decay mechanism (λ) ensures adaptability to evolving fraud tactics without retraining, while PCA handles feature correlation. In extensive experiments on five real datasets, using both synthetic and real labels, SATrade achieved 99 % AUC, 93 % F-measure, and 0.2 ms/record latency, which is a significant improvement over the six baseline methods. The framework’s interpretability allows tracing anomalies to fraudulent behaviors like sudden order spikes. The constant memory consumption of 0.25 MB per record and linear scalability make SATrade suitable for high-frequency environments and online platforms. |
| ArticleNumber | 102119 |
| Author | Hashemi Golpayegani, Alireza Khodabandehlou, Samira |
| Author_xml | – sequence: 1 givenname: Samira orcidid: 0000-0003-3618-1926 surname: Khodabandehlou fullname: Khodabandehlou, Samira email: s.khodabandehlou@aut.ac.ir organization: Department of Computer Engineering, Hamedan University of Technology, Hamedan, Iran – sequence: 2 givenname: Alireza surname: Hashemi Golpayegani fullname: Hashemi Golpayegani, Alireza email: sa.hashemi@aut.ac.ir organization: APA Research Center & Department of Information Technology Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran |
| BookMark | eNp9kM1OAyEURonRxL--gQsewKnAFNrZmBij1qTRhbomt3BRxpmhAdrEtxcdY1y5gnzc7-Ryjsn-EAYk5IyzKWdcXbTTFlM2b1PBhCyR4LzZI0dCcFmxZqH2_9wPySSlljHGmzIm1RF5fwg77GiPOXqTKAyWrp6WFLrXEH1-6xN1IdLtkLYbjDuf0J7TiNBV2fdYxkMP3Qe1mNFkHwbqB9pvu-wrSJsSUQsZaMql0qdTcuCgSzj5OU_Iy-3N8_WyWj3e3V9frSpTqyZXaj1H6QAkWlwY5lzjHBNOcSFtI6Xi5U1JBou1qG1tncUaGS8l5MjnVtQn5H7k2gCt3kTfQ_zQAbz-DkJ81RCzNx1qU0ssTBRSsBkKtlZzNQMHtasXs7UwhTUbWSaGlCK6Xx5n-su_bvXoX3_516P_Ursca1j-ufMYdTIeB4PWx6KlLOL_B3wCXq-UAw |
| Cites_doi | 10.1016/j.dss.2016.09.003 10.1145/276698.276876 10.1016/j.knosys.2015.07.013 10.1145/2133360.2133363 10.1002/hf2.10026 10.1145/3494564 10.1007/s13278-024-01262-5 10.1016/j.eswa.2016.10.051 10.1007/s10618-023-00960-6 10.1109/TNSM.2020.3037019 10.1017/9781108684163 10.1016/j.ipm.2023.103306 10.1016/j.eswa.2011.04.066 10.1109/TII.2021.3139363 10.1016/j.eswa.2021.116225 10.1504/IJECRM.2016.082187 10.1145/342009.335388 10.1016/j.physa.2018.09.011 10.1016/j.neucom.2019.03.006 10.1007/s10614-021-10131-8 10.1145/1541880.1541882 10.1007/s10618-007-0076-8 10.21105/joss.01336 10.1145/2523813 10.1109/TNNLS.2015.2480959 10.1145/3641857 10.1109/ACCESS.2021.3100359 10.1016/j.dss.2010.08.006 10.1007/s10618-024-01060-9 10.1145/3442381.3450023 10.1016/j.procs.2019.01.007 10.1016/j.cose.2015.09.005 10.1109/CEC.2019.8789938 10.1016/j.jalgor.2003.12.001 10.1016/j.jnca.2015.11.016 10.1007/s10618-023-00999-5 |
| ContentType | Journal Article |
| Copyright | 2025 The Authors |
| Copyright_xml | – notice: 2025 The Authors |
| DBID | 6I. AAFTH AAYXX CITATION DOA |
| DOI | 10.1016/j.jestch.2025.102119 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2215-0986 |
| ExternalDocumentID | oai_doaj_org_article_c35e955e25204e20b6764afa3f384b2c 10_1016_j_jestch_2025_102119 S2215098625001740 |
| GroupedDBID | 0R~ 4.4 457 5VS 6I. AAEDT AAEDW AAFTH AAIKJ AALRI AAXUO AAYWO ABMAC ACGFS ADBBV ADEZE ADVLN AEXQZ AFJKZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ APXCP BCNDV EBS EJD FDB GROUPED_DOAJ HZ~ IPNFZ IXB KQ8 M41 M~E O9- OK1 RIG ROL SSZ AAYXX CITATION |
| ID | FETCH-LOGICAL-c369t-6b7e5faa5ede8c0ff9ff02f6125d95561faa650a8b23d3dfde3e01b7ee1e17d23 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001530632000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2215-0986 |
| IngestDate | Fri Oct 03 12:41:56 EDT 2025 Sat Oct 25 04:55:36 EDT 2025 Sat Nov 15 16:53:39 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Real-time anomaly detection Stream mining Market manipulation detection Multi-aspect data Locality-sensitive hashing Unsupervised learning |
| Language | English |
| License | This is an open access article under the CC BY license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c369t-6b7e5faa5ede8c0ff9ff02f6125d95561faa650a8b23d3dfde3e01b7ee1e17d23 |
| ORCID | 0000-0003-3618-1926 |
| OpenAccessLink | https://doaj.org/article/c35e955e25204e20b6764afa3f384b2c |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c35e955e25204e20b6764afa3f384b2c crossref_primary_10_1016_j_jestch_2025_102119 elsevier_sciencedirect_doi_10_1016_j_jestch_2025_102119 |
| PublicationCentury | 2000 |
| PublicationDate | September 2025 2025-09-00 2025-09-01 |
| PublicationDateYYYYMMDD | 2025-09-01 |
| PublicationDate_xml | – month: 09 year: 2025 text: September 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Engineering science and technology, an international journal |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | Bartos, Mullapudi, Troutman (b0270) 2019; 4 Khodabandehlou, Alireza Hashemi Golpayegani (b0045) 2022; 30 S.C. Tan, K.M. Ting, T.F. Liu. Fast anomaly detection for streaming data. In: Proceedings of the Twenty-Second international joint conference on Artificial Intelligence. Barcelona, Catalonia, Spain: AAAI Press; 2011. p. 1511–6. (IJCAI’11). Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel (b0265) 2011; 12 Shi, Sun, Shen, Cheng (b0190) 2019; 1 Azadifar, Rostami, Berahmand, Moradi, Oussalah (b0080) 2022; 1 Zhai, Cao, Yao, Ding, Li (b0035) 2017; 1 Lin, Cheng, Zeng, Huo, Zhang, Wang (b0275) 2024; 73 Zhang, Dou, He, Zhou, Leckie, Kotagiri (b0170) 2017 Cormode, Muthukrishnan (b0250) 2005; 55 Leangarun, Tangamchit, Thajchayapong (b0140) 2018 Uslu, Akal (b0125) 2022; 60 J. Leskovec, A. Rajaraman, J.D. Ullman. Higher Education from Cambridge University Press. Cambridge University Press; 2020 [cited 2025 May 18]. Mining of Massive Datasets. Golmohammadi, Zaiane (b0180) 2015 W. Litwin. Linear hashing: a new tool for file and table addressing. In: Proceedings of the sixth international conference on Very Large Data Bases - Volume 6. Montreal, Quebec, Canada: VLDB Endowment; 1980. p. 212–23. Duan, Zhang, Tong, Lu, Lv, Hou (b0090) 2024; 38 Heyden, Fouché, Arzamasov, Fenn, Kalinke, Böhm (b0060) 2024; 38 Ngai, Hu, Wong, Chen, Sun (b0100) 2011; 50 Martínez Miranda, Phelps, Howard (b0195) 2019; 2 Scaranti, Carvalho, Barbon, Lloret, Proença (b0230) 2022; 191 Chen, Xu, Zheng, Zhou, Yang, Detecting (b0185) 2019 P. Indyk, R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the thirtieth annual ACM symposium on Theory of computing [Internet]. New York, NY, USA: Association for Computing Machinery; 1998 [cited 2025 May 17]. p. 604–13. (STOC ’98). Available from: https://dl.acm.org/doi/10.1145/276698.276876. Cui, Gao (b0200) 2023; 4 Chandola, Banerjee, Kumar (b0245) 2009; 41 . S. Bhatia, A. Jain, P. Li, R. Kumar, B. Hooi. MStream: Fast Anomaly Detection in Multi-Aspect Streams. In: Proceedings of the Web Conference 2021 [Internet]. New York, NY, USA: Association for Computing Machinery; 2021 [cited 2023 Jun 17]. p. 3371–82. (WWW ’21). Available from: https://dl.acm.org/doi/10.1145/3442381.3450023. Wang, Xu, Huang, Yang (b0025) 2019; 28 Bansal, Sharma (b0050) 2024; 38 Sadgali, Sael, Benabbou (b0105) 2019; 1 Diaz, Theodoulidis, Sampaio (b0010) 2011; 38 Fanaee-T, Gama (b0055) 2015; 1 Liu, Ting, Zhou (b0215) 2012; 6 S. Guha, N. Mishra, G. Roy, O. Schrijvers. Robust Random Cut Forest Based Anomaly Detection on Streams. In: Proceedings of The 33rd International Conference on Machine Learning [Internet]. PMLR; 2016 [cited 2023 Jun 17]. p. 2712–21. Available from Khodabandehlou, NikNafs (b0135) 2016; 10 Khodabandehlou, Hashemi Golpayegani (b0150) 2024; 14 Alexander, Cumming (b0005) 2020 Sinayobye, Kiwanuka, Kaawaase (b0110) 2018 B. Rizvi, A. Belatreche, A. Bouridane. A Dendritic Cell Immune System Inspired Approach for Stock Market Manipulation Detection. In: 2019 IEEE Congress on Evolutionary Computation (CEC) [Internet]. Wellington, New Zealand: IEEE; 2019 [cited 2020 Apr 23]. p. 3325–32. Available from Sheikhpour, Berahmand, Forouzandeh (b0130) 2023; 7 Zeng, Xiao, Lin, Luo, Lin (b0075) 2023; 60 Qi, Yang, Zhou, Rafique, Ma (b0070) 2022; 18 Ahmed, Naser Mahmood, Hu (b0040) 2016; 1 Chullamonthon, Tangamchit (b0160) 2023; 15 Palshikar, Apte (b0175) 2008; 16 Liu, Li, Shi (b0205) 2024; 15 Eswaran, SedanSpot (b0095) 2018 Putina, Rossi (b0065) 2021; 18 Golmohammadi, Zaiane, Díaz (b0120) 2014 Cao, Li, Coleman, Belatreche, McGinnity (b0020) 2016; 27 Leangarun, Tangamchit, Thajchayapong (b0155) 2021; 9 Bhatia, Liu, Hooi, Yoon, Shin, Faloutsos (b0085) 2022; 16 M.M. Breunig, H.P. Kriegel, R.T. Ng, J. Sander. LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data [Internet]. New York, NY, USA: Association for Computing Machinery; 2000 [cited 2023 Jun 17]. p. 93–104. (SIGMOD ’00). Zhai, Cao, Yao, Ding, Li (b0030) 2017; 1 Gama, Žliobaitė, Bifet, Pechenizkiy, Bouchachia (b0260) 2014; 46 Khodabandehlou, Golpayegani (b0015) 2024; 18 West, Bhattacharya (b0115) 2016; 1 Alexander (10.1016/j.jestch.2025.102119_b0005) 2020 Gama (10.1016/j.jestch.2025.102119_b0260) 2014; 46 Zeng (10.1016/j.jestch.2025.102119_b0075) 2023; 60 10.1016/j.jestch.2025.102119_b0255 Sheikhpour (10.1016/j.jestch.2025.102119_b0130) 2023; 7 Khodabandehlou (10.1016/j.jestch.2025.102119_b0135) 2016; 10 Martínez Miranda (10.1016/j.jestch.2025.102119_b0195) 2019; 2 Cormode (10.1016/j.jestch.2025.102119_b0250) 2005; 55 Khodabandehlou (10.1016/j.jestch.2025.102119_b0045) 2022; 30 Bansal (10.1016/j.jestch.2025.102119_b0050) 2024; 38 Ahmed (10.1016/j.jestch.2025.102119_b0040) 2016; 1 Putina (10.1016/j.jestch.2025.102119_b0065) 2021; 18 Zhang (10.1016/j.jestch.2025.102119_b0170) 2017 Ngai (10.1016/j.jestch.2025.102119_b0100) 2011; 50 Diaz (10.1016/j.jestch.2025.102119_b0010) 2011; 38 Duan (10.1016/j.jestch.2025.102119_b0090) 2024; 38 10.1016/j.jestch.2025.102119_b0225 Heyden (10.1016/j.jestch.2025.102119_b0060) 2024; 38 10.1016/j.jestch.2025.102119_b0220 cr-split#-10.1016/j.jestch.2025.102119_b0165.2 Leangarun (10.1016/j.jestch.2025.102119_b0140) 2018 cr-split#-10.1016/j.jestch.2025.102119_b0165.1 Bartos (10.1016/j.jestch.2025.102119_b0270) 2019; 4 Fanaee-T (10.1016/j.jestch.2025.102119_b0055) 2015; 1 Golmohammadi (10.1016/j.jestch.2025.102119_b0120) 2014 cr-split#-10.1016/j.jestch.2025.102119_b0210.1 cr-split#-10.1016/j.jestch.2025.102119_b0210.2 Lin (10.1016/j.jestch.2025.102119_b0275) 2024; 73 Eswaran (10.1016/j.jestch.2025.102119_b0095) 2018 Liu (10.1016/j.jestch.2025.102119_b0205) 2024; 15 Palshikar (10.1016/j.jestch.2025.102119_b0175) 2008; 16 Chullamonthon (10.1016/j.jestch.2025.102119_b0160) 2023; 15 10.1016/j.jestch.2025.102119_b0235 Sadgali (10.1016/j.jestch.2025.102119_b0105) 2019; 1 Shi (10.1016/j.jestch.2025.102119_b0190) 2019; 1 Scaranti (10.1016/j.jestch.2025.102119_b0230) 2022; 191 Chen (10.1016/j.jestch.2025.102119_b0185) 2019 Khodabandehlou (10.1016/j.jestch.2025.102119_b0015) 2024; 18 Golmohammadi (10.1016/j.jestch.2025.102119_b0180) 2015 Bhatia (10.1016/j.jestch.2025.102119_b0085) 2022; 16 Azadifar (10.1016/j.jestch.2025.102119_b0080) 2022; 1 Liu (10.1016/j.jestch.2025.102119_b0215) 2012; 6 Pedregosa (10.1016/j.jestch.2025.102119_b0265) 2011; 12 Leangarun (10.1016/j.jestch.2025.102119_b0155) 2021; 9 Cui (10.1016/j.jestch.2025.102119_b0200) 2023; 4 cr-split#-10.1016/j.jestch.2025.102119_b0145.1 cr-split#-10.1016/j.jestch.2025.102119_b0145.2 Zhai (10.1016/j.jestch.2025.102119_b0030) 2017; 1 Uslu (10.1016/j.jestch.2025.102119_b0125) 2022; 60 Khodabandehlou (10.1016/j.jestch.2025.102119_b0150) 2024; 14 10.1016/j.jestch.2025.102119_b0240 Cao (10.1016/j.jestch.2025.102119_b0020) 2016; 27 Wang (10.1016/j.jestch.2025.102119_b0025) 2019; 28 West (10.1016/j.jestch.2025.102119_b0115) 2016; 1 Sinayobye (10.1016/j.jestch.2025.102119_b0110) 2018 Zhai (10.1016/j.jestch.2025.102119_b0035) 2017; 1 Qi (10.1016/j.jestch.2025.102119_b0070) 2022; 18 Chandola (10.1016/j.jestch.2025.102119_b0245) 2009; 41 |
| References_xml | – volume: 27 start-page: 2351 year: 2016 end-page: 2363 ident: b0020 article-title: Detecting wash trade in financial market using digraphs and dynamic programming publication-title: IEEE Trans. Neural Networks Learn. Syst. – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: b0265 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – start-page: 1 year: 2015 end-page: 10 ident: b0180 article-title: Time series contextual anomaly detection for detecting market manipulation in stock market publication-title: In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) – volume: 28 start-page: 46 year: 2019 end-page: 58 ident: b0025 article-title: Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning publication-title: Neurocomputing – volume: 1 start-page: 47 year: 2016 end-page: 66 ident: b0115 article-title: Intelligent financial fraud detection: a comprehensive review publication-title: Comput. Secur. – start-page: 435 year: 2014 end-page: 441 ident: b0120 article-title: Detecting stock market manipulation using supervised learning algorithms publication-title: In: 2014 International Conference on Data Science and Advanced Analytics (DSAA) – reference: S.C. Tan, K.M. Ting, T.F. Liu. Fast anomaly detection for streaming data. In: Proceedings of the Twenty-Second international joint conference on Artificial Intelligence. Barcelona, Catalonia, Spain: AAAI Press; 2011. p. 1511–6. (IJCAI’11). – volume: 1 start-page: 26 year: 2017 end-page: 41 ident: b0030 article-title: Computational intelligent hybrid model for detecting disruptive trading activity publication-title: Decis. Support Syst. – volume: 38 start-page: 501 year: 2024 end-page: 534 ident: b0090 article-title: An anomaly aware network embedding framework for unsupervised anomalous link detection publication-title: Data Min. Knowl. Disc. – volume: 14 start-page: 98 year: 2024 ident: b0150 article-title: How do abnormal trading behaviors diffuse in electronic markets? publication-title: Soc. Netw. Anal. Min. – volume: 4 start-page: 1336 year: 2019 ident: b0270 article-title: rrcf: implementation of the robust random cut forest algorithm for anomaly detection on streams publication-title: J. Open Source Softw. – volume: 1 start-page: 332 year: 2015 end-page: 345 ident: b0055 article-title: Multi-aspect-streaming tensor analysis publication-title: Knowl.-Based Syst. – volume: 18 start-page: 839 year: 2021 end-page: 854 ident: b0065 article-title: Online anomaly detection leveraging stream-based clustering and real-time telemetry publication-title: IEEE Trans. Netw. Serv. Manag. – start-page: 293 year: 2019 end-page: 2935 ident: b0185 article-title: “Pump Dump Schemes” on Cryptocurrency Market using an improved Apriori Algorithm publication-title: In: 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE) – start-page: 953 year: 2018 end-page: 958 ident: b0095 article-title: Detecting Anomalies in Edge Streams publication-title: In: 2018 IEEE International Conference on Data Mining (ICDM) – volume: 4 year: 2023 ident: b0200 article-title: WTEYE: on-chain wash trade detection and quantification for ERC20 cryptocurrencies publication-title: Blockchain: Res. Appl. – volume: 9 start-page: 106824 year: 2021 end-page: 106838 ident: b0155 article-title: Stock price manipulation detection using deep unsupervised learning: the case of Thailand publication-title: IEEE Access – reference: M.M. Breunig, H.P. Kriegel, R.T. Ng, J. Sander. LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data [Internet]. New York, NY, USA: Association for Computing Machinery; 2000 [cited 2023 Jun 17]. p. 93–104. (SIGMOD ’00). – volume: 38 start-page: 12757 year: 2011 end-page: 12771 ident: b0010 article-title: Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices publication-title: Expert Syst. Appl. – reference: B. Rizvi, A. Belatreche, A. Bouridane. A Dendritic Cell Immune System Inspired Approach for Stock Market Manipulation Detection. In: 2019 IEEE Congress on Evolutionary Computation (CEC) [Internet]. Wellington, New Zealand: IEEE; 2019 [cited 2020 Apr 23]. p. 3325–32. Available from: – volume: 38 start-page: 3831 year: 2024 end-page: 3867 ident: b0050 article-title: Statistical methods utilizing structural properties of time-evolving networks for event detection publication-title: Data Min. Knowl. Disc. – volume: 191 year: 2022 ident: b0230 article-title: Unsupervised online anomaly detection in software defined network environments publication-title: Expert Syst. Appl. – volume: 15 year: 2023 ident: b0160 article-title: Ensemble of supervised and unsupervised deep neural networks for stock price manipulation detection publication-title: Expert Syst. Appl. – volume: 16 start-page: 75:1 year: 2022 end-page: 75:22 ident: b0085 article-title: Real-time anomaly detection in edge streams publication-title: ACM Trans. Knowl. Discov. Data – volume: 1 start-page: 45 year: 2019 end-page: 54 ident: b0105 article-title: Performance of machine learning techniques in the detection of financial frauds publication-title: Procedia Comput. Sci. – reference: P. Indyk, R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the thirtieth annual ACM symposium on Theory of computing [Internet]. New York, NY, USA: Association for Computing Machinery; 1998 [cited 2025 May 17]. p. 604–13. (STOC ’98). Available from: https://dl.acm.org/doi/10.1145/276698.276876. – volume: 73 start-page: 1 year: 2024 end-page: 16 ident: b0275 article-title: Low-rank and sparse representation inspired interpretable network for hyperspectral anomaly detection publication-title: IEEE Trans. Instrum. Meas. – volume: 60 start-page: 25 year: 2022 end-page: 45 ident: b0125 article-title: A machine learning approach to detection of trade-based manipulations in Borsa Istanbul publication-title: Comput. Econ. – start-page: 2104 year: 2018 end-page: 2111 ident: b0140 article-title: Stock Price Manipulation Detection using Generative Adversarial Networks publication-title: In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI) – start-page: 983 year: 2017 end-page: 994 ident: b0170 article-title: LSHiForest: a Generic Framework for Fast tree Isolation based Ensemble Anomaly Analysis publication-title: In: 2017 IEEE 33rd International Conference on Data Engineering – volume: 1 start-page: 19 year: 2016 end-page: 31 ident: b0040 article-title: A survey of network anomaly detection techniques publication-title: J. Netw. Comput. Appl. – volume: 55 start-page: 58 year: 2005 end-page: 75 ident: b0250 article-title: An improved data stream summary: the count-min sketch and its applications publication-title: J. Algorithms – volume: 60 year: 2023 ident: b0075 article-title: Double locality sensitive hashing Bloom filter for high-dimensional streaming anomaly detection publication-title: Inf. Process. Manag. – volume: 50 start-page: 559 year: 2011 end-page: 569 ident: b0100 article-title: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature publication-title: Decis. Support Syst. – volume: 6 start-page: 3:1 year: 2012 end-page: 3:39 ident: b0215 article-title: Isolation-based Anomaly Detection publication-title: ACM Trans. Knowl. Discov. Data – volume: 30 year: 2022 ident: b0045 article-title: Market manipulation detection: a systematic literature review publication-title: Expert Syst. Appl. – volume: 7 year: 2023 ident: b0130 article-title: Hessian-based semi-supervised feature selection using generalized uncorrelated constraint publication-title: Knowl.-Based Syst. – reference: J. Leskovec, A. Rajaraman, J.D. Ullman. Higher Education from Cambridge University Press. Cambridge University Press; 2020 [cited 2025 May 18]. Mining of Massive Datasets. – volume: 46 start-page: 44:1 year: 2014 end-page: 44:37 ident: b0260 article-title: A survey on concept drift adaptation publication-title: ACM Comput. Surv. – volume: 18 start-page: 6503 year: 2022 end-page: 6511 ident: b0070 article-title: Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0 publication-title: IEEE Trans. Ind. Inf. – reference: S. Guha, N. Mishra, G. Roy, O. Schrijvers. Robust Random Cut Forest Based Anomaly Detection on Streams. In: Proceedings of The 33rd International Conference on Machine Learning [Internet]. PMLR; 2016 [cited 2023 Jun 17]. p. 2712–21. Available from: – reference: S. Bhatia, A. Jain, P. Li, R. Kumar, B. Hooi. MStream: Fast Anomaly Detection in Multi-Aspect Streams. In: Proceedings of the Web Conference 2021 [Internet]. New York, NY, USA: Association for Computing Machinery; 2021 [cited 2023 Jun 17]. p. 3371–82. (WWW ’21). Available from: https://dl.acm.org/doi/10.1145/3442381.3450023. – volume: 41 start-page: 15:1 year: 2009 end-page: 15:58 ident: b0245 article-title: Anomaly detection: a survey publication-title: ACM Comput. Surv. – start-page: 11 year: 2018 end-page: 19 ident: b0110 article-title: A State-of-the-Art Review of Machine Learning Techniques for Fraud Detection Research publication-title: In: 2018 IEEE/ACM Symposium on Software Engineering in Africa (SEiA) – volume: 10 start-page: 158 year: 2016 end-page: 178 ident: b0135 article-title: Improving customer loyalty evaluation methods in the grocery retail industry: a data mining approach publication-title: Int. J. Electron. Customer Relationship Manage. – volume: 18 start-page: 111:1 year: 2024 end-page: 111:29 ident: b0015 article-title: FiFrauD: unsupervised financial fraud detection in dynamic graph streams publication-title: ACM Trans. Knowl. Discov. Data – volume: 16 start-page: 135 year: 2008 end-page: 164 ident: b0175 article-title: Collusion set detection using graph clustering publication-title: Data Min. Knowl. Disc. – volume: 15 year: 2024 ident: b0205 article-title: A stock price manipulation detecting model with ensemble learning publication-title: Expert Syst. Appl. – reference: . – volume: 2 start-page: 4 year: 2019 end-page: 36 ident: b0195 article-title: Order flow dynamics for prediction of order cancelation and applications to detect market manipulation publication-title: High Freq. – start-page: 624 p year: 2020 ident: b0005 publication-title: Corruption and Fraud in Financial Markets: Malpractice, Misconduct and Manipulation – volume: 1 start-page: 225 year: 2017 end-page: 238 ident: b0035 article-title: Coarse and fine identification of collusive clique in financial market publication-title: Expert Syst. Appl. – volume: 38 start-page: 1334 year: 2024 end-page: 1363 ident: b0060 article-title: Adaptive Bernstein change detector for high-dimensional data streams publication-title: Data Min. Knowl. Disc. – reference: W. Litwin. Linear hashing: a new tool for file and table addressing. In: Proceedings of the sixth international conference on Very Large Data Bases - Volume 6. Montreal, Quebec, Canada: VLDB Endowment; 1980. p. 212–23. – volume: 1 year: 2022 ident: b0080 article-title: Graph-based relevancy-redundancy gene selection method for cancer diagnosis publication-title: Comput. Biol. Med. – volume: 1 start-page: 565 year: 2019 end-page: 571 ident: b0190 article-title: Detect colluded stock manipulation via clique in trading network publication-title: Phys. A – volume: 1 start-page: 26 issue: 93 year: 2017 ident: 10.1016/j.jestch.2025.102119_b0030 article-title: Computational intelligent hybrid model for detecting disruptive trading activity publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2016.09.003 – ident: 10.1016/j.jestch.2025.102119_b0235 doi: 10.1145/276698.276876 – volume: 1 start-page: 332 issue: 89 year: 2015 ident: 10.1016/j.jestch.2025.102119_b0055 article-title: Multi-aspect-streaming tensor analysis publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2015.07.013 – ident: #cr-split#-10.1016/j.jestch.2025.102119_b0165.2 – volume: 6 start-page: 3:1 issue: 1 year: 2012 ident: 10.1016/j.jestch.2025.102119_b0215 article-title: Isolation-based Anomaly Detection publication-title: ACM Trans. Knowl. Discov. Data doi: 10.1145/2133360.2133363 – volume: 7 issue: 269 year: 2023 ident: 10.1016/j.jestch.2025.102119_b0130 article-title: Hessian-based semi-supervised feature selection using generalized uncorrelated constraint publication-title: Knowl.-Based Syst. – volume: 2 start-page: 4 issue: 1 year: 2019 ident: 10.1016/j.jestch.2025.102119_b0195 article-title: Order flow dynamics for prediction of order cancelation and applications to detect market manipulation publication-title: High Freq. doi: 10.1002/hf2.10026 – start-page: 2104 year: 2018 ident: 10.1016/j.jestch.2025.102119_b0140 article-title: Stock Price Manipulation Detection using Generative Adversarial Networks – start-page: 1 year: 2015 ident: 10.1016/j.jestch.2025.102119_b0180 article-title: Time series contextual anomaly detection for detecting market manipulation in stock market – volume: 16 start-page: 75:1 issue: 4 year: 2022 ident: 10.1016/j.jestch.2025.102119_b0085 article-title: Real-time anomaly detection in edge streams publication-title: ACM Trans. Knowl. Discov. Data doi: 10.1145/3494564 – volume: 14 start-page: 98 issue: 1 year: 2024 ident: 10.1016/j.jestch.2025.102119_b0150 article-title: How do abnormal trading behaviors diffuse in electronic markets? publication-title: Soc. Netw. Anal. Min. doi: 10.1007/s13278-024-01262-5 – volume: 1 start-page: 225 issue: 69 year: 2017 ident: 10.1016/j.jestch.2025.102119_b0035 article-title: Coarse and fine identification of collusive clique in financial market publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.10.051 – volume: 38 start-page: 501 issue: 2 year: 2024 ident: 10.1016/j.jestch.2025.102119_b0090 article-title: An anomaly aware network embedding framework for unsupervised anomalous link detection publication-title: Data Min. Knowl. Disc. doi: 10.1007/s10618-023-00960-6 – volume: 18 start-page: 839 issue: 1 year: 2021 ident: 10.1016/j.jestch.2025.102119_b0065 article-title: Online anomaly detection leveraging stream-based clustering and real-time telemetry publication-title: IEEE Trans. Netw. Serv. Manag. doi: 10.1109/TNSM.2020.3037019 – ident: 10.1016/j.jestch.2025.102119_b0255 doi: 10.1017/9781108684163 – volume: 60 issue: 3 year: 2023 ident: 10.1016/j.jestch.2025.102119_b0075 article-title: Double locality sensitive hashing Bloom filter for high-dimensional streaming anomaly detection publication-title: Inf. Process. Manag. doi: 10.1016/j.ipm.2023.103306 – start-page: 624 p year: 2020 ident: 10.1016/j.jestch.2025.102119_b0005 – start-page: 983 year: 2017 ident: 10.1016/j.jestch.2025.102119_b0170 article-title: LSHiForest: a Generic Framework for Fast tree Isolation based Ensemble Anomaly Analysis – volume: 38 start-page: 12757 issue: 10 year: 2011 ident: 10.1016/j.jestch.2025.102119_b0010 article-title: Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.04.066 – volume: 30 issue: 210 year: 2022 ident: 10.1016/j.jestch.2025.102119_b0045 article-title: Market manipulation detection: a systematic literature review publication-title: Expert Syst. Appl. – ident: #cr-split#-10.1016/j.jestch.2025.102119_b0210.2 – ident: 10.1016/j.jestch.2025.102119_b0225 – volume: 18 start-page: 6503 issue: 9 year: 2022 ident: 10.1016/j.jestch.2025.102119_b0070 article-title: Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0 publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2021.3139363 – volume: 191 year: 2022 ident: 10.1016/j.jestch.2025.102119_b0230 article-title: Unsupervised online anomaly detection in software defined network environments publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116225 – start-page: 953 year: 2018 ident: 10.1016/j.jestch.2025.102119_b0095 article-title: Detecting Anomalies in Edge Streams – volume: 10 start-page: 158 issue: 2–4 year: 2016 ident: 10.1016/j.jestch.2025.102119_b0135 article-title: Improving customer loyalty evaluation methods in the grocery retail industry: a data mining approach publication-title: Int. J. Electron. Customer Relationship Manage. doi: 10.1504/IJECRM.2016.082187 – ident: #cr-split#-10.1016/j.jestch.2025.102119_b0210.1 doi: 10.1145/342009.335388 – start-page: 11 year: 2018 ident: 10.1016/j.jestch.2025.102119_b0110 article-title: A State-of-the-Art Review of Machine Learning Techniques for Fraud Detection Research – volume: 1 start-page: 565 issue: 513 year: 2019 ident: 10.1016/j.jestch.2025.102119_b0190 article-title: Detect colluded stock manipulation via clique in trading network publication-title: Phys. A doi: 10.1016/j.physa.2018.09.011 – ident: 10.1016/j.jestch.2025.102119_b0240 – volume: 73 start-page: 1 year: 2024 ident: 10.1016/j.jestch.2025.102119_b0275 article-title: Low-rank and sparse representation inspired interpretable network for hyperspectral anomaly detection publication-title: IEEE Trans. Instrum. Meas. – volume: 28 start-page: 46 issue: 347 year: 2019 ident: 10.1016/j.jestch.2025.102119_b0025 article-title: Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.03.006 – volume: 60 start-page: 25 issue: 1 year: 2022 ident: 10.1016/j.jestch.2025.102119_b0125 article-title: A machine learning approach to detection of trade-based manipulations in Borsa Istanbul publication-title: Comput. Econ. doi: 10.1007/s10614-021-10131-8 – volume: 4 issue: 1 year: 2023 ident: 10.1016/j.jestch.2025.102119_b0200 article-title: WTEYE: on-chain wash trade detection and quantification for ERC20 cryptocurrencies publication-title: Blockchain: Res. Appl. – volume: 41 start-page: 15:1 issue: 3 year: 2009 ident: 10.1016/j.jestch.2025.102119_b0245 article-title: Anomaly detection: a survey publication-title: ACM Comput. Surv. doi: 10.1145/1541880.1541882 – volume: 16 start-page: 135 issue: 2 year: 2008 ident: 10.1016/j.jestch.2025.102119_b0175 article-title: Collusion set detection using graph clustering publication-title: Data Min. Knowl. Disc. doi: 10.1007/s10618-007-0076-8 – volume: 4 start-page: 1336 issue: 35 year: 2019 ident: 10.1016/j.jestch.2025.102119_b0270 article-title: rrcf: implementation of the robust random cut forest algorithm for anomaly detection on streams publication-title: J. Open Source Softw. doi: 10.21105/joss.01336 – volume: 46 start-page: 44:1 issue: 4 year: 2014 ident: 10.1016/j.jestch.2025.102119_b0260 article-title: A survey on concept drift adaptation publication-title: ACM Comput. Surv. doi: 10.1145/2523813 – volume: 27 start-page: 2351 issue: 11 year: 2016 ident: 10.1016/j.jestch.2025.102119_b0020 article-title: Detecting wash trade in financial market using digraphs and dynamic programming publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2015.2480959 – volume: 15 issue: 248 year: 2024 ident: 10.1016/j.jestch.2025.102119_b0205 article-title: A stock price manipulation detecting model with ensemble learning publication-title: Expert Syst. Appl. – volume: 18 start-page: 111:1 issue: 5 year: 2024 ident: 10.1016/j.jestch.2025.102119_b0015 article-title: FiFrauD: unsupervised financial fraud detection in dynamic graph streams publication-title: ACM Trans. Knowl. Discov. Data doi: 10.1145/3641857 – volume: 9 start-page: 106824 year: 2021 ident: 10.1016/j.jestch.2025.102119_b0155 article-title: Stock price manipulation detection using deep unsupervised learning: the case of Thailand publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3100359 – volume: 50 start-page: 559 issue: 3 year: 2011 ident: 10.1016/j.jestch.2025.102119_b0100 article-title: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2010.08.006 – ident: #cr-split#-10.1016/j.jestch.2025.102119_b0145.2 – volume: 12 start-page: 2825 issue: 85 year: 2011 ident: 10.1016/j.jestch.2025.102119_b0265 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – volume: 38 start-page: 3831 issue: 6 year: 2024 ident: 10.1016/j.jestch.2025.102119_b0050 article-title: Statistical methods utilizing structural properties of time-evolving networks for event detection publication-title: Data Min. Knowl. Disc. doi: 10.1007/s10618-024-01060-9 – ident: #cr-split#-10.1016/j.jestch.2025.102119_b0165.1 doi: 10.1145/3442381.3450023 – volume: 1 start-page: 45 issue: 148 year: 2019 ident: 10.1016/j.jestch.2025.102119_b0105 article-title: Performance of machine learning techniques in the detection of financial frauds publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2019.01.007 – volume: 15 issue: 220 year: 2023 ident: 10.1016/j.jestch.2025.102119_b0160 article-title: Ensemble of supervised and unsupervised deep neural networks for stock price manipulation detection publication-title: Expert Syst. Appl. – volume: 1 start-page: 47 issue: 57 year: 2016 ident: 10.1016/j.jestch.2025.102119_b0115 article-title: Intelligent financial fraud detection: a comprehensive review publication-title: Comput. Secur. doi: 10.1016/j.cose.2015.09.005 – ident: #cr-split#-10.1016/j.jestch.2025.102119_b0145.1 doi: 10.1109/CEC.2019.8789938 – start-page: 435 year: 2014 ident: 10.1016/j.jestch.2025.102119_b0120 article-title: Detecting stock market manipulation using supervised learning algorithms – ident: 10.1016/j.jestch.2025.102119_b0220 – volume: 55 start-page: 58 issue: 1 year: 2005 ident: 10.1016/j.jestch.2025.102119_b0250 article-title: An improved data stream summary: the count-min sketch and its applications publication-title: J. Algorithms doi: 10.1016/j.jalgor.2003.12.001 – volume: 1 issue: 147 year: 2022 ident: 10.1016/j.jestch.2025.102119_b0080 article-title: Graph-based relevancy-redundancy gene selection method for cancer diagnosis publication-title: Comput. Biol. Med. – volume: 1 start-page: 19 issue: 60 year: 2016 ident: 10.1016/j.jestch.2025.102119_b0040 article-title: A survey of network anomaly detection techniques publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2015.11.016 – start-page: 293 year: 2019 ident: 10.1016/j.jestch.2025.102119_b0185 article-title: “Pump Dump Schemes” on Cryptocurrency Market using an improved Apriori Algorithm – volume: 38 start-page: 1334 issue: 3 year: 2024 ident: 10.1016/j.jestch.2025.102119_b0060 article-title: Adaptive Bernstein change detector for high-dimensional data streams publication-title: Data Min. Knowl. Disc. doi: 10.1007/s10618-023-00999-5 |
| SSID | ssj0001921156 |
| Score | 2.3019938 |
| Snippet | Given a vast online stream of transactions in e-markets, how can we detect fraudulent traders and suspicious behaviors in an unsupervised manner? Can we detect... |
| SourceID | doaj crossref elsevier |
| SourceType | Open Website Index Database Publisher |
| StartPage | 102119 |
| SubjectTerms | Locality-sensitive hashing Market manipulation detection Multi-aspect data Real-time anomaly detection Stream mining Unsupervised learning |
| Title | Novel metrics and LSH algorithms for unsupervised, real-time anomaly detection in multi-aspect data streams |
| URI | https://dx.doi.org/10.1016/j.jestch.2025.102119 https://doaj.org/article/c35e955e25204e20b6764afa3f384b2c |
| Volume | 69 |
| WOSCitedRecordID | wos001530632000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2215-0986 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001921156 issn: 2215-0986 databaseCode: DOA dateStart: 20140101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2215-0986 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001921156 issn: 2215-0986 databaseCode: M~E dateStart: 20140101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELZQxQAD4inKSx4YsXDsOo8REFWHUiEBElvkxGdoadOKtEgs_HZ8dosywcKSIXHs6M7yfad89x0h5xDbDhi3eY3DwgwBOyusyBhXqUHNJ5eSeZ3ZfjIYpM_P2X2j1RdywoI8cDDcZSkVZEqBUIJ3QPAiTuKOtlpamXYKUeLpy5OskUyNAm5xUCde1cp5QtfIHbP-_4NQKFgQobhOIxZ5yf5GSGqEme422VriQ3oVvmuHrEG1SzYbqoF75G0w_YAxnWAvrLKmujK0_9CjevwydZn-66SmDojSRVUvZngQ1GAuqIOGY4Z95N3w6USPP6mBuWdhVXRYUU8rZNqXXVIkjVKsIdGTep88dW8fb3ps2TOBlTLO5iwuElBWawUG0pJbm1nLhUUcYzJshemeOVCm00JII401IIFH7iWIIEqMkAekVU0rOCS0sAioilgaTLoioSPNMwFurjTiVpk2YSvr5bMgjZGvOGOjPFg7R2vnwdptco0m_hmLwtb-hnN3vnR3_pe72yRZOShfYoQQ-91Uw1-XP_qP5Y_JBk4ZSGYnpDV_X8ApWS8_5sP6_czvQXe9-7r9BnNz4zc |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Novel+metrics+and+LSH+algorithms+for+unsupervised%2C+real-time+anomaly+detection+in+multi-aspect+data+streams&rft.jtitle=Engineering+science+and+technology%2C+an+international+journal&rft.au=Samira+Khodabandehlou&rft.au=Alireza+Hashemi+Golpayegani&rft.date=2025-09-01&rft.pub=Elsevier&rft.eissn=2215-0986&rft.volume=69&rft.spage=102119&rft_id=info:doi/10.1016%2Fj.jestch.2025.102119&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_c35e955e25204e20b6764afa3f384b2c |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2215-0986&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2215-0986&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2215-0986&client=summon |