Asmar Muath, Tuqan Alia
Department of Finance, Faculty of Business and Communication, An-Najah National University, Nablus, Palestine.
Master of Business Administration, Faculty of Graduate Studies, An-Najah National University, Nablus, Palestine.
Heliyon. 2024 Sep 6;10(17):e37571. doi: 10.1016/j.heliyon.2024.e37571. eCollection 2024 Sep 15.
Cybersecurity continues to be an important concern for financial institutions given the technology's rapid development and increasing adoption of digital services. Effective safety measures must be adopted to safeguard sensitive financial data and protect clients from potential harm due to the rise in cyber threats that target digital organizations. The aim of this study is to investigates how machine learning algorithms are integrated into cyber security measures in the context of digital banking and its benefits and drawbacks. We initially provide a general overview of digital banks and the particular security concerns that differentiate them from conventional banks. Then, we explore the value of machine learning in strengthening cybersecurity defenses. We revealed that insider threats, distributed denial of service (DDoS) assaults, ransomware, phishing attacks, and social engineering are main cyberthreats that are digital banks exposed. We identify the appropriate machine learning algorithms such as support vector machines (SVM), recurrent neural networks (RNN), hidden markov models (HMM), and local outlier factor (LOF) that are used for detection and prevention cyberthreats. In addition, we provide a model that considers ethical concerns while constructing a cybersecurity framework to address potential vulnerabilities in digital banking systems. The advantages and disadvantages of incorporating machine learning into the cybersecurity strategy of digital banks are outlined using strengths, weaknesses, opportunities, threats (SWOT) analysis. This study seeks to provide a thorough knowledge of how machine learning may strengthen cybersecurity procedures, protect digital banks, and maintain customer trust in the ecosystem of digital banking.
鉴于技术的快速发展以及数字服务采用率的不断提高，网络安全仍然是金融机构的一个重要关注点。必须采取有效的安全措施来保护敏感的金融数据，并保护客户免受针对数字组织的网络威胁增加所带来的潜在危害。本研究的目的是调查在数字银行背景下机器学习算法如何融入网络安全措施及其利弊。我们首先对数字银行以及使其有别于传统银行的特定安全问题进行了总体概述。然后，我们探讨了机器学习在加强网络安全防御方面的价值。我们发现内部威胁、分布式拒绝服务（DDoS）攻击、勒索软件、网络钓鱼攻击和社会工程是数字银行面临的主要网络威胁。我们确定了用于检测和预防网络威胁的适当机器学习算法，如支持向量机（SVM）、递归神经网络（RNN）、隐马尔可夫模型（HMM）和局部离群因子（LOF）。此外，我们提供了一个在构建网络安全框架以解决数字银行系统潜在漏洞时考虑道德问题的模型。使用优势、劣势、机会、威胁（SWOT）分析概述了将机器学习纳入数字银行网络安全策略的优缺点。本研究旨在全面了解机器学习如何加强网络安全程序、保护数字银行并在数字银行生态系统中维持客户信任。
