Security Evaluation of a Banking Fraud Analysis System
| Autor: | Carminati, Michele, Polino, Mario, Continella, Andrea, Lanzi, Andrea, Maggi, Federico, Zanero, Stefano |
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| Rok vydání: | 2018 |
| Předmět: |
fraud and anomaly detection
Security analysis Decision support system General Computer Science Computer science spending pattern granularity analysis 02 engineering and technology Computer security computer.software_genre Online banking Robustness (computer science) 020204 information systems 0202 electrical engineering electronic engineering information engineering Detection performance Malware 020201 artificial intelligence & image processing Granularity Online banking fraud and anomaly detection spending pattern granularity analysis mimicry attack mimicry attack Safety Risk Reliability and Quality computer |
| Zdroj: | ACM Transactions on Privacy and Security |
| ISSN: | 2471-2566 |
| DOI: | 10.1145/3178370 |
| Popis: | The significant growth of banking fraud, fueled by the underground economy of malware, has raised the need for effective detection systems. Therefore, in the last few years, banks have upgraded their security to protect transactions from fraud. State-of-the-art solutions detect fraud as deviations from customers’ spending habits. To the best of our knowledge, almost all existing approaches do not provide an in-depth model’s granularity and security analysis against elusive attacks. In this article, we examine Banksealer, a decision support system for banking fraud analysis that evaluates the influence on detection performance of the granularity at which spending habits are modeled and its security against evasive attacks. First, we compare user-centric modeling, which builds a model for each user, with system-centric modeling, which builds a model for the entire system, from the point of view of detection performance. Then, we assess the robustness of Banksealer against malicious attackers that are aware of the structure of the models in use. To this end, we design and implement a proof-of-concept attack tool that performs mimicry attacks, emulating a sophisticated attacker that cloaks frauds to avoid detection. We experimentally confirm the feasibility of such attacks, their cost, and the effort required by an attacker in order to perform them. In addition, we discuss possible countermeasures. We provide a comprehensive evaluation on a large real-world dataset obtained from one of the largest Italian banks. |
| Databáze: | OpenAIRE |
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