Advanced security testing using a cyber-attack forecasting model: A case study of financial institutions
As the number of cyber-attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides the...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad a Distancia de Madrid (UDIMA) |
| Repositorio: | udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid |
| OAI Identifier: | oai:udimundus.udima.es:20.500.12226/1879 |
| Acceso en línea: | http://hdl.handle.net/20.500.12226/1879 |
| Access Level: | acceso abierto |
| Palabra clave: | Seguridad CIberataque Ciberseguridad Instituciones financieras |
| Sumario: | As the number of cyber-attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber-security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network-based cyber-attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber-attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber-crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures. |
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