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Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data.
Tracking dirty proceeds: exploring data mining technologies as tools to investigate money laundering
R Cory Watkins, K Michael Reynolds, Ron Demara, Michael Georgiopoulos, Avelino Gonzalez, and Ron Eaglin · 2003
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Shilling recommender systems for fun and profit
Shyong K Lam and John Riedl · 2004
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Limited knowledge shilling attacks in collaborative filtering systems
Robin Burke, Bamshad Mobasher, and Runa Bhaumik · 2005
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Developing an intelligent data discriminating system of anti-money laundering based on svm
Jun Tang and Jian Yin · 2005
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A rbf neural network model for anti-money laundering
Lin-Tao Lv, Na Ji, and Jiu-Long Zhang · 2008
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Active learning through sequential design, with applications to detection of money laundering
Xinwei Deng, V Roshan Joseph, Agus Sudjianto, and CF Jeff Wu · 2009
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Application of cluster-based local outlier factor algorithm in anti-money laundering
Zengan Gao · 2009
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Research on money laundering detection based on improved minimum spanning tree clustering and its application
Xingqi Wang and Guang Dong · 2009
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Suspicious activity reporting using dynamic bayesian networks
Saleha Raza and Sajjad Haider · 2011
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Reviews, reputation, and revenue: The case of yelp.com
Michael Luca · 2016
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Detection of money laundering groups using supervised learning in networks
David Savage, Qingmai Wang, Pauline Chou, Xiuzhen Zhang, and Xinghuo Yu · 2016
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A new algorithm for money laundering detection based on structural similarity
Reza Soltani, Uyen Trang Nguyen, Yang Yang, Mohammad Faghani, Alaa Yagoub, and Aijun An · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Finding suspicious activities in financial transactions and distributed ledgers
Ramiro Daniel Camino, Radu State, Leandro Montero, and Petko Valtchev · 2017
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Machine learning techniques for anti-money laundering (aml) solutions in suspicious transaction detection: a review
Zhiyuan Chen, Ee Na Teoh, Amril Nazir, Ettikan Kandasamy Karuppiah, Kim Sim Lam, et al · 2018
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Joana Lorenz, Maria Inês Silva, David Aparício, João Tiago Ascensão, and Pedro Bizarro · 2020
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Revisiting adversarially learned injection attacks against recommender systems
Jiaxi Tang, Hongyi Wen, and Ke Wang · 2020
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Adversarial attacks and defenses in images, graphs and text: A review
Han Xu, Yao Ma, Hao-Chen Liu, Debayan Deb, Hui Liu, Ji-Liang Tang, and Anil K. Jain · 2020
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Synthetic data generation for fraud detection using gans
Charitos Charitou, Simo Dragicevic, and Artur d’Avila Garcez · 2021
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Anti-money laundering in the eu: Time to get serious. ceps task force report 28 jan 2021., January 2021
Karel Lannoo and Richard Parlour · 2021
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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Scalable graph learning for anti-money laundering: A first look
Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E Leiserson, and Tao B Schardl · 2018
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Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems
Muhammad Usama, Muhammad Asim, Siddique Latif, Junaid Qadir, and Ala-Al-Fuqaha · 2019
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Deep reinforcement adversarial learning against botnet evasion attacks
Giovanni Apruzzese, Mauro Andreolini, Mirco Marchetti, Andrea Venturi, and Michele Colajanni · 2020
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BAE: BERT-based adversarial examples for text classification
Siddhant Garg and Goutham Ramakrishnan · 2020
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Detecting money laundering transactions with machine learning
Martin Jullum, Anders Løland, Ragnar Bang Huseby, Geir Ånonsen, and Johannes Lorentzen · 2020
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Flowscope: Spotting money laundering based on graphs
Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, and Xueqi Cheng · 2020
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Guiltywalker: Distance to illicit nodes in the bitcoin network
Catarina Oliveira, João Torres, Maria Inês Silva, David Aparício, João Tiago Ascensão, and Pedro Bizarro · 2021
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It and operational spending in aml-kyc: 2021 edition, December 2021
Arin Ray · 2021
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Cubeflow: Money laundering detection with coupled tensors
Xiaobing Sun, Jiabao Zhang, Qiming Zhao, Shenghua Liu, Jinglei Chen, Ruoyu Zhuang, Huawei Shen, and Xueqi Cheng · 2021
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Ready for emerging threats to recommender systems? a graph convolution-based generative shilling attack
Fan Wu, Min Gao, Junliang Yu, Zongwei Wang, Kecheng Liu, and Xu Wang · 2021
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Attacking recommender systems with plausible profile
Xuxin Zhang, Jian Chen, Rui Zhang, Chen Wang, and Ling Liu · 2021
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Shilling black-box recommender systems by learning to generate fake user profiles
Chen Lin, Si Chen, Meifang Zeng, Sheng Zhang, Min Gao, and Hui Li · 2022
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Controllable data generation by deep learning: A review
Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, and Liang Zhao · 2022
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