No abstract

Financial transactions lay up the foundation of modern society. Unfortunately, illicit abuse of the financial system is pervasive. Fraud controls aim to detect these suspicious activities, but they require deep analysis to model their efficacy and value proposition. Due to the private nature and scale of these financial transactions, this analysis is often performed in hindsight. Financial institutions lack of information, due to the hidden fraud problem, to properly set and tune their fraud controls systems. This is probably one of the reasons we are losing the war against crime. This paper presents PaySim, a cutting edge agent-based model that simulates financial fraud scenarios to improve current fraud controls. PaySim uses aggregated anonymized data from a real financial dataset to generate synthetic data that closely resembles the transactions dynamics, statistical properties and causal dynamics observed in the original dataset, while incorporating any malicious behaviour of interest. Using an agent-based framework specifically designed to cover the demands of financial simulation and the application of mathematical statistics, we leverage a real-life scenario based on a known fraud scheme to demonstrate the advantage of simulated data over real-world data when setting adequate controls for fraud detection.

Fraud controls for financial transactions are needed and required by law enforcement agencies to flag suspicious criminal activity. These controls however require deeper analysis of the effectiveness and the negative impact for the legal customers. Due to the intrinsically private nature of financial transactions this analysis is often performed after several months of actively using fraud controls. In this paper, we present an analysis of different fraud prevention controls on a mobile money service based on thresholds using a simulator called PaySim. PaySim uses aggregated data from a sample dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour. With technology frameworks such as Agent-Based simulation techniques, and the application of mathematical statistics, we show in this paper that the simulated data can be as prudent as the original dataset for setting optimal controls for fraud detection.

Cybersecurity decisions are made across a range of social, technical, economic, regulatory and political domains. There is a gap between what companies and institutions plan to do while developing their internal IS-related policies and what should be done according to a multi-stakeholder system perspective in this area. Our task as researchers is to bridge this gap by offering potential solutions. The aim of our work is to promote the usage of the socio-technical systems approach to support the emerging role of systems thinking in cybersecurity education, using simulation as a supporting tool for learning. Meanwhile, new trends in cybersecurity curricula suggest an important shift toward new thinking approaches such as adversarial and systems thinking. We explored individuals' adversarial and systems thinking skills in an open agent-based simulated environment and subsequently assessed the impact based on a participant survey. We discuss these results and point to directions for further investigation. The second contribution of the article is the provision of a tool for developing target users' skills in making quantitative risk decisions and giving them a deeper understanding of the importance and use of key indices in the cyber risk management process.

We hardly pass any day without hearing of a new cyber attack. The recent ever-increasing occurrence of such attacks has given to researchers, practitioners and others an opportunity to raise awareness and train staff from the public and private institutions, as well as other people within the society, about the evolving nature of cyberspace threats. As a first step in this process, we aim to present main findings from a pilot study conducted with a target group of Master students with diverse backgrounds and knowledge about cyber security practices. The study was done using an agent-based simulation tool, CyberAIMs, as the core component of the experiment. Students were involved in a pre-test/post-test study in order to assess the probable change in their thinking process after using CyberAIMs. A scenario created from a real cyber case was additionally used to get the participants accustomed to the tool. The experiment is still in progress, while preliminary data indicate that there is a shift in students' perspective on the most relevant attributes affecting defense agents' performance, results that could be related to both adversarial and systems thinking processes.