FIST: A Structured Threat Modeling Framework for Fraud Incidents

Abstract: Fraudulent activities are rapidly evolving, employing increasingly diverse and sophisticated methods that pose serious threats to individuals, organizations, and society. This paper proposes the FIST Framework (Fraud Incident Structured Threat Framework), an innovative structured threat modeling methodology specifically designed for fraud scenarios. Inspired by MITRE ATT&CK and DISARM, FIST systematically incorporates social engineering tactics, stage-based behavioral decomposition, and detailed attack technique mapping into a reusable knowledge base. FIST aims to enhance the efficiency of fraud detection and the standardization of threat intelligence sharing, promoting collaboration and a unified language across organizations and sectors. The framework integrates interdisciplinary insights from cybersecurity, criminology, and behavioral science, addressing both technical vectors and psychological manipulation mechanisms in fraud. This approach enables fine-grained analysis of fraud incidents, supporting automated detection, quantitative risk assessment, and standardized incident reporting. The effectiveness of the framework is further validated through real-world case studies, demonstrating its value in bridging academic research and practical applications, and laying the foundation for an intelligence-driven anti-fraud ecosystem. To the best of our knowledge, FIST is the first systematic, open-source fraud threat modeling framework that unifies both technical and psychological aspects, and is made freely available to foster collaboration between academia and industry.

• The paper presents the FIST framework that systematically integrates technical and psychological tactics to model fraud incidents.
• It outlines a four-phase process with 9 tactics and 93 techniques, enabling detailed mapping of fraud operations.
• The framework enhances threat intelligence sharing and supports scalable, AI-driven fraud detection strategies.

Overview of "FIST: A Structured Threat Modeling Framework for Fraud Incidents"

"FIST: A Structured Threat Modeling Framework for Fraud Incidents" proposes a novel framework specifically designed for the nuanced landscape of fraud threat modeling, emphasizing both technical and psychological elements of fraudulent activities. The framework distinguishes itself by systematically incorporating social engineering tactics and allowing for a detailed attack technique mapping. This dual focus addresses the escalating complexity of fraud scenarios that conventional cybersecurity frameworks inadequately capture.

Introduction

Recent advancements in digital communication infrastructures have inadvertently amplified the sophistication and reach of fraudulent activities. Current threat modeling frameworks, such as MITRE ATT&CK, while invaluable for cybersecurity, often fail to fully encapsulate the dynamic and multi-staged nature of fraud, heavily reliant on psychological manipulation and social engineering. FIST aims to bridge this gap by integrating interdisciplinary insights from cybersecurity, criminology, and behavioral science, proposing an innovative solution to systematize and standardize threat intelligence related to fraud.

The FIST Framework

Design and Structure

FIST is structured into four distinct operational phases: Preparation, Promotion, Engagement, and Concealment. These phases represent the comprehensive lifecycle of a fraud operation, facilitating detailed analysis and understanding. Each phase is populated with specific tactics and techniques vital for identifying and countering fraud operations. This hierarchical structure supports modular expansions, allowing organizations to adapt to evolving fraud tactics dynamically.

Components and Scale

The framework encompasses 4 operational phases, 9 major tactics, and 93 detailed techniques, incorporating both detection patterns and mitigation strategies. This enables organizations to effectively map and monitor fraud activities at different stages, enhancing strategic responses and intelligence sharing.

Technical and Psychological Integration

FIST uniquely combines both technical vectors, such as AI-generated fake personas, with psychological tactics, including urgency inducement. This dual-track approach presents a comprehensive view of contemporary fraud operations, providing actionable insights into both technology-driven and human-centric fraud techniques.

Case Study: Application to Investment Fraud

A case study involving an investment fraud scheme executed via social media illustrates FIST's practical application. Fraudsters utilized fake personas and deceptive tactics to lure victims into fraudulent investment schemes. The framework effectively mapped these actions through its phases, allowing for the identification of key detection points and introducing enhanced monitoring strategies. This demonstrates the framework's capability to dissect fraudulent operations and inform defensive postures.

Applications and Implications

FIST's structured approach provides several practical utilities:

• Standardization of threat intelligence sharing across sectors, promoting collaborative defenses against fraud.
• Enhancement of automated fraud detection mechanisms via structured mapping and indicator identification.
• Support for the development of scalable fraud detection databases and intelligence platforms.

Moreover, FIST lays the groundwork for AI-based analytics, enabling advanced incident analysis and response strategies. By offering a structured dataset and consistent taxonomy, it facilitates the integration of automated and expert-driven tracking methodologies.

Conclusion and Future Directions

The FIST framework presents a comprehensive, interdisciplinary method for fraud threat modeling, offering a robust platform for analyzing and combating sophisticated fraud incidents. Its modular and extensible design ensures adaptability to the continuously evolving fraud landscape while promoting cross-sector collaboration for intelligence sharing.

Future endeavors will focus on expanding the framework's catalog of techniques and indicators, enhancing its integration capabilities with existing threat modeling frameworks, and conducting empirical validations through collaborative engagements with industry and academic entities. Building on its open-source foundation, FIST aims to further its impact on the development of innovative fraud detection and prevention strategies, reinforcing the effectiveness and scalability of anti-fraud defenses. Through ongoing refinement and community involvement, FIST promises to contribute significantly to the advancement of anti-fraud research and operations.