Silver bank: vulnerability and risks during cyberattacks

Horna, César Jhonnatan; Toro, Leonardo; Regalado-Pezúa, Otto

doi:10.1108/eemcs-02-2021-0034

Cited by 3 publications

(1 citation statement)

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“…Businesses lost almost $666 million to cyberattacks in 2003 which does not include incidental damages from theft of property and identity (Bhasin, 2007, Bada andNurse, 2020). Protecting against cyber threats is essential to ensure the integrity of financial systems and maintain trust in the banking sector (Horna, Toro, and Regalado-Pezua, 2022). Disruptions caused by cyberattacks can cripple banking operations and severely impact customer experience (AL-Bloushi, 2020, Lis andMendel, 2019).…”

Section: Introductionmentioning

confidence: 99%

Cybersecurity Risk Assessment in Banking: Methodologies and Best Practices

Samuel Onimisi Dawodu,

Adedolapo Omotosho,

Odunayo Josephine Akindote

et al. 2023

Comput. sci. IT res. j.

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Cybersecurity risk assessment in banking is the process of identifying, analyzing, and evaluating the cyber threats and vulnerabilities that may affect the confidentiality, integrity, and availability of the information systems and data of banks and their customers. Cybersecurity risk assessment in banking helps banks to prioritize and implement appropriate controls and measures to mitigate the cyber risks and to comply with the relevant regulations and standards. This study focusses on identifying effective risk assessment strategies, highlighting how they can be adapted and applied in various banking environments, especially in developing economies like Nigeria. As the banking industry continues to evolve in the digital era, the significance of robust cybersecurity measures cannot be overstated. This paper delves into the critical domain of Cybersecurity Risk Assessment in Banking, exploring various methodologies and best practices employed to safeguard financial institutions against evolving cyber threats. The dynamic landscape of cyber risks faced by banks, ranging from sophisticated malware and phishing attacks to insider threats and system vulnerabilities are examined. The paper provides an in-depth analysis of established and emerging methodologies for conducting effective cybersecurity risk assessments in the banking sector. It explores quantitative and qualitative risk assessment approaches, threat modeling, and scenario analysis, shedding light on their respective strengths and limitations. Moreover, the document highlights the importance of aligning risk assessment methodologies with industry regulations and compliance standards to ensure a comprehensive and regulatory-compliant cybersecurity framework. Best practices for cybersecurity risk management in banking are scrutinized, emphasizing the integration of proactive threat intelligence, continuous monitoring, and incident response planning. The role of advanced technologies, including artificial intelligence and machine learning, in enhancing the efficiency of risk assessment processes is also discussed. Furthermore, the paper addresses the human element in cybersecurity, emphasizing the significance of training and awareness programs to mitigate risks associated with human error and social engineering attacks. By synthesizing insights from industry practices, regulatory guidelines, and technological advancements, this paper offers a comprehensive guide for banking professionals, cybersecurity practitioners, and policymakers involved in fortifying the resilience of financial institutions against cyber threats. Ultimately, the research aims to contribute to the ongoing discourse on cybersecurity risk assessment in banking, providing actionable insights to navigate the complex landscape of digital risks and ensuring the continued trust and security of the financial ecosystem. Keywords: Cybersecurity; Risk Assessment; Banking; Methodologies; Cyber Threat; Artificial Intelligence; Best Practices

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Section: Introductionmentioning

confidence: 99%

Cybersecurity Risk Assessment in Banking: Methodologies and Best Practices

Samuel Onimisi Dawodu,

Adedolapo Omotosho,

Odunayo Josephine Akindote

et al. 2023

Comput. sci. IT res. j.

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Developing Maturity Matrix: Measuring Banking Sector Readiness in Digital Corporate Governance

Budiwiyono Lie,

Samopa,

Hari Ginardi

2024

2024 IEEE International Symposium on Consumer Technology (ISCT)

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Analysis of Mathematical Models for Countering Cyber Fraud in Banks

Kuzmenko

Yarovenko

Skrynka

2022

VSDU

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The article is devoted to the current topic of analysis of mathematical models for countering cyber fraud in banks. This problem is due to the security risks growth in the banking system, which are formed by fraudsters' cyberattacks and cybercrimes implementation. Therefore, the priority task for cyberbanking security is the application of modern mathematical methods to analyse the sources of cyber attacks, identify threats and losses in the banking services market, identify cyber-attacks and assess the scenario of potential cyber risk, etc. The article analyses the most widespread types of cyber fraud: social engineering, phishing, stalking, farming, DoS attacks, online fraud, potentially unwanted programs, etc. The study also considered a model of cognitive computing and detection of suspicious transactions in banking cyber-physical systems based on quantum computing in BCPS for the post-quantum era. The advantages, disadvantages and results of the model are defined. Predictive modelling is proposed to detect fraud in real-time by analysing incoming bank transactions with payment cards. Within the framework of this method, such models are used for the classification of fraud detection as logistic regression, a decision tree, and a narrower technique - a random forest decision tree. The study also considered using the harmonic search algorithm in neural networks to improve fraud detection in the banking system. It is found that although this model has the advantage of learning ability based on past behaviour, there are difficulties in the long-term processing of many neural networks. The stages of model implementation are also given. In addition, the modelling of credit card fraud detection is based on using two types of models: supervised and unsupervised. Supervised models include logistic regression, K-nearest neighbours, and extreme gradient boosting. The one-class support vector model, restricted Boltzmann model, and generative-competitive network are considered among uncontrolled generative models.

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Silver bank: vulnerability and risks during cyberattacks

Cited by 3 publications

References 0 publications

Cybersecurity Risk Assessment in Banking: Methodologies and Best Practices

Cybersecurity Risk Assessment in Banking: Methodologies and Best Practices

Developing Maturity Matrix: Measuring Banking Sector Readiness in Digital Corporate Governance

Analysis of Mathematical Models for Countering Cyber Fraud in Banks

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