As digital scamming explodes in Southeast Asia, including so called "pig butchering" investment scams, the United Nations Office on Drugs and Crime (UNODC) issued a comprehensive report this week with a dire warning about the rapid growth of this criminal ecosystem. Many digital scams have traditionally relied on social engineering, or tricking victims into giving away their money willingly, rather than leaning on malware or other highly technical methods. But researchers have increasingly sounded the alarm that scammers are incorporating generative AI content and deepfakes to expand the scale and effectiveness of their operations. And the UN report offers the clearest evidence yet that these high tech tools are turning an already urgent situation into a crisis. In addition to buying written scripts to use with potential victims or relying on templates for malicious websites, attackers have increasingly been leaning on generative AI platforms to create communication content in multiple languages and deepfake generators that can create photos or even video of nonexistent people to show victims and enhance verisimilitude.

This survey paper offers a thorough analysis of techniques and algorithms used in the identification of crime leaders within criminal networks. For each technique, the paper examines its effectiveness, limitations, potential for improvement, and future prospects. The main challenge faced by existing survey papers focusing on algorithms for identifying crime leaders and predicting crimes is effectively categorizing these algorithms. To address this limitation, this paper proposes a new methodological taxonomy that hierarchically classifies algorithms into more detailed categories and specific techniques. The paper includes empirical and experimental evaluations to rank the different techniques. The combination of the methodological taxonomy, empirical evaluations, and experimental comparisons allows for a nuanced and comprehensive understanding of the techniques and algorithms for identifying crime leaders, assisting researchers in making informed decisions. Moreover, the paper offers valuable insights into the future prospects of techniques for identifying crime leaders, emphasizing potential advancements and opportunities for further research. Here's an overview of our empirical analysis findings and experimental insights, along with the solution we've devised: (1) PageRank and Eigenvector centrality are reliable for mapping network connections, (2) Katz Centrality can effectively identify influential criminals through indirect links, stressing their significance in criminal networks, (3) current models fail to account for the specific impacts of criminal influence levels, the importance of socio-economic context, and the dynamic nature of criminal networks and hierarchies, and (4) we propose enhancements, such as incorporating temporal dynamics and sentiment analysis to reflect the fluidity of criminal activities and relationships, which could improve the detection of key criminals .

In order to model criminal networks for law enforcement purposes, a limited supply of data needs to be translated into validated agent-based models. What is missing in current criminological modelling is a systematic and transparent framework for modelers and domain experts that establishes a modelling procedure for computational criminal modelling that includes translating qualitative data into quantitative rules. For this, we propose FREIDA (Framework for Expert-Informed Data-driven Agent-based models). Throughout the paper, the criminal cocaine replacement model (CCRM) will be used as an example case to demonstrate the FREIDA methodology. For the CCRM, a criminal cocaine network in the Netherlands is being modelled where the kingpin node is being removed, the goal being for the remaining agents to reorganize after the disruption and return the network into a stable state. Qualitative data sources such as case files, literature and interviews are translated into empirical laws, and combined with the quantitative sources such as databases form the three dimensions (environment, agents, behaviour) of a networked ABM. Four case files are being modelled and scored both for training as well as for validation scores to transition to the computational model and application phase respectively. In the last phase, iterative sensitivity analysis, uncertainty quantification and scenario testing eventually lead to a robust model that can help law enforcement plan their intervention strategies. Results indicate the need for flexible parameters as well as additional case file simulations to be performed.

Network structure provides critical information for understanding the dynamic behavior of networks. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation-Maximization-Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.