Recognizing roles of online illegal gambling participants: An ensemble learning approach. Issue 87 (November 2019)

• Record Type: Journal Article
• Authors: Han, Xiaohui; Wang, Lianhai; Xu, Shujiang; Zhao, Dawei; Liu, Guangqi
• Is Part Of: Computers & security. Issue 87(2019)
• Publication Date: 2019-11
• Subjects: Online gambling -- Role recognition -- Cybercrime -- Monetary transaction -- Ensemble learning; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2019.101588
• Languages: English
• ISSNs: 0167-4048

Abstract

Highlights: We present an automatic PRR approach which learns a supervised classifier based on monetary transaction data to predict the roles of IOG participants. To the best of our knowledge, this is the first computational intelligence based technique to tackle PRR problem in an IOG ecosystem. We propose two sets of features, i.e. transaction statistical features and network structural features, to effectively represent participants. These features can capture both behavior patterns and structural importance of participants. We adopt an ensemble learning strategy in the training phase of the PRR classifier to reduce the impact of unbalanced training data. We evaluate the performance of the proposed approach using real-world IOG monetary transaction data. Experimental results demonstrate the feasibility and validity of the proposed approach. Abstract: Online gambling has exploded into a substantial global industry over the past two decades. Despite its prosperity, online gambling is explicitly prohibited or restricted in most countries due to social problems it caused. This has given rise to black markets of illegal online gambling (IOG) which are replete with criminal potential. To help criminal investigators find key members of an IOG organization and fight against them, in this study, we propose an ensemble learning approach named EC4PRR to automatically identify the roles that IOG participants play in their ecosystem. We extract two categories of features, i.e., transaction statistical features (TSF) and network structural features (NSF), from monetary transaction data effectively represent participants. Since the number of participants in different roles is typically unbalanced, we train the classifier with a combination of under-sampling and ensemble learning strategies to reduce the impact of imbalanced training data. Experiments were carried out with real-world case data. Experimental results demonstrate the validity of the proposed features as well as the feasibility of EC4PRR.