The impacts of information revolution are omnipresent from life to work. The web services have significantly changed our living styles in daily life, such as Facebook for communication and Wikipedia for knowledge acquirement. Besides, varieties of information systems, such as data management system and management information system, make us work more efficiently. However, it is usually a double-edged sword. With the popularity of web services, relevant security issues are arising, such as fake news on Facebook and vandalism on Wikipedia, which definitely impose severe security threats to OSNs and their legitimate participants. Likewise, office automation incurs another challenging security issue, insider threat, which may involve the theft of confidential information, the theft of intellectual property, or the sabotage of computer systems. A recent survey says that 27% of all cyber crime incidents are suspected to be committed by the insiders. As a result, how to flag out these malicious web users or insiders is urgent. The fast development of machine learning (ML) techniques offers an unprecedented opportunity to build some ML models that can assist humans to detect the individuals who conduct misbehaviors automatically. However, unlike some static outlier detection scenarios where ML models have achieved promising performance, the malicious behaviors conducted by humans are often dynamic. Such dynamic behaviors lead to various unique challenges of dynamic fraud detection:
To address the aforementioned challenges, in this dissertation, we conduct a systematic study for dynamic fraud detection, with a focus on: (1) Unavailability of labeled data: we present (a) a few-shot learning framework to handle the extremely imbalanced dataset that abnormal samples are far fewer than the normal ones and (b) a one-class fraud detection method using a complementary GAN (Generative Adversarial Network) to adaptively generate potential abnormal samples; (2) Lack of high-quality labels: we develop a neural survival analysis model for fraud early detection to deal with the time gap; (3) Time-evolving nature: we propose (a) a hierarchical neural temporal point process model and (b) a dynamic Dirichlet marked Hawkes process model for fraud detection.
|Commitee:||Li, Qinghua, Yang, Song, Zhang, Lu|
|School:||University of Arkansas|
|School Location:||United States -- Arkansas|
|Source:||DAI-B 82/2(E), Dissertation Abstracts International|
|Keywords:||Dirichlet process, Fraud detection, Machine learning, Mixture model, Sequential model, Survival analysis|
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