Enhancing Law Enforcement Training: A Gamified Approach to Detecting Terrorism Financing

Paper: arXiv 2403.13625

Abstract

Tools for fighting cyber-criminal activities using new technologies are promoted and deployed every day. However, too often, they are unnecessarily complex and hard to use, requiring deep domain and technical knowledge. These characteristics often limit the engagement of law enforcement and end-users in these technologies that, despite their potential, remain misunderstood. For this reason, in this study, we describe our experience in combining learning and training methods and the potential benefits of gamification to enhance technology transfer and increase adult learning. In fact, in this case, participants are experienced practitioners in professions/industries that are exposed to terrorism financing (such as Law Enforcement Officers, Financial Investigation Officers, private investigators, etc.) We define training activities on different levels for increasing the exchange of information about new trends and criminal modus operandi among and within law enforcement agencies, intensifying cross-border cooperation and supporting efforts to combat and prevent terrorism funding activities. On the other hand, a game (hackathon) is designed to address realistic challenges related to the dark net, crypto assets, new payment systems and dark web marketplaces that could be used for terrorist activities. The entire methodology was evaluated using quizzes, contest results, and engagement metrics. In particular, training events show about 60% of participants complete the 11-week training course, while the Hackathon results, gathered in two pilot studies (Madrid and The Hague), show increasing expertise among the participants (progression in the achieved points on average). At the same time, more than 70% of participants positively evaluate the use of the gamification approach, and more than 85% of them consider the implemented Use Cases suitable for their investigations.

Complexity vs Empirical Score

• Math Complexity: 0.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a qualitative and quantitative evaluation of a gamified training program, using metrics like completion rates and participant surveys, but lacks advanced mathematical modeling or backtested financial strategies.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Enhance Law Enforcement Training<br>to Detect Terrorism Financing"] --> B["Methodology Design<br>Gamification & Hackathon"]

  B --> C["Training & Data Inputs<br>Workshops, Use Cases, Crypto/Darknet Simulations"]
  
  C --> D["Computational/Engagement Process<br>Interactive Challenges & Skill Assessment"]

  D --> E["Pilot Execution<br>The Hague & Madrid Studies"]

  E --> F["Evaluation Metrics<br>Quizzes, Contest Results, Engagement Data"]

  F --> G["Key Findings<br>60% Course Completion, >70% Positive Feedback<br>>85% Use Case Suitability"]

Cited References (38)

1. Towards Artificial Intelligence-Based Cybersecurity: The Practices and ChatGPT Generated Ways to Combat Cybercrime (2023) — link
2. A CTF-Based Approach in Cyber Security Education for Secondary School Students (2021) — link
3. Illicit firearms and other weapons on darknet markets (2021) — link
4. GraphSense: A General-Purpose Cryptoasset Analytics Platform (2021) — link
5. Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges (2021) — link
6. This is the way to teach: insights from academics and students about assessment that supports learning (2018) — link
7. TITANIUM: Tools for the Investigation of Transactions in Underground Markets (2018) — link
8. Can artificial intelligence help in the war on cybercrime (2017) — link

0 of 38 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.