Detecting Open Banking API Security Threats Using Bayesian Attack Graphs

Dawood Behbehani, M. Rajarajan, N. Komninos, Khalid Al–Begain
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Abstract

Particularly amid Covid-19, enterprises' digital transformation has rapidly accelerated, making cybersecurity an even bigger challenge. Financial institutions adopt FinTech technologies to advance their service and achieve an enhanced customer experience that creates a competitive edge in the market. FinTech products utilise open banking API services to allow communication between a financial institution and a FinTech provider. However, such an integration introduces significant security concerns. Therefore, financial firms must ensure that a robust API service to protect the bank's infrastructure and its customers' information. To address this concern, we propose a Framework for Open Banking API security that utilises STRIDE model to identify security threats in FinTech integration via Open Banking API and Bayesian Attack Graphs to automate predictions of the most exploitable attack paths.
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利用贝叶斯攻击图检测开放银行API安全威胁
特别是在新冠疫情背景下,企业数字化转型加速,网络安全面临更大挑战。金融机构采用金融科技技术来提升他们的服务,并实现增强的客户体验,从而在市场上创造竞争优势。金融科技产品利用开放式银行API服务,允许金融机构和金融科技提供商之间进行通信。然而,这样的集成引入了重要的安全问题。因此,金融公司必须确保一个健壮的API服务来保护银行的基础设施及其客户的信息。为了解决这一问题,我们提出了一个开放银行API安全框架,该框架利用STRIDE模型通过开放银行API和贝叶斯攻击图识别金融科技集成中的安全威胁,以自动预测最易利用的攻击路径。
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