Impact of Machine Learning-Driven Cyber Threat Detection on Network Security Performance in Financial Institutions in Japan
DOI:
https://doi.org/10.47941/ijce.3275Keywords:
Machine Learning-Driven Cyber Threat Detection, Network Security Performance, Financial InstitutionsAbstract
Purpose: The purpose of this article was to analyze impact of machine learning-driven cyber threat detection on network security performance in financial institutions.
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: Machine learning-driven cyber threat detection significantly improves network security performance in financial institutions by enhancing threat detection rates, reducing false positives, and speeding up incident response times. Deep learning and ensemble models have proven effective at detecting advanced threats, but their success depends on data quality, model retraining, and organizational readiness. While these systems offer substantial benefits, challenges such as computational requirements, model transparency, and ethical concerns must be addressed for optimal implementation.
Unique Contribution to Theory, Practice and Policy: Technology-organization-environment (TOE) framework, dynamic capability theory & information processing theory (IPT) may be used to anchor future studies on the impact of machine learning-driven cyber threat detection on network security performance in financial institutions. Financial institutions should prioritize the deployment of hybrid ML models (e.g., combining supervised and deep learning algorithms) to optimize both detection accuracy and response speed across a variety of threat vectors. Policymakers and regulators should create sector-specific AI guidelines for cybersecurity, addressing issues like data privacy, algorithm bias, and model accountability in financial institutions.
Downloads
References
Ahmed, M., Mahmood, A. N., & Hu, J. (2020). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 161, 102630. https://doi.org/10.1016/j.jnca.2020.102630
Al-Shboul, M., Rababah, O., Gharleghi, B., & Marashdeh, Z. (2021). Dynamic capabilities and cybersecurity: The role of big data analytics. Computers & Security, 105, 102238. https://doi.org/10.1016/j.cose.2021.102238
Kumar, R., & Singh, R. (2020). Machine learning-based cyber threat detection: A comparative study. Procedia Computer Science, 167, 1841–1850. https://doi.org/10.1016/j.procs.2020.03.202
Liu, Y., Peng, H., Wu, D., & Zheng, X. (2020). Machine learning and deep learning methods for cybersecurity. IEEE Access, 8, 124295–124308. https://doi.org/10.1109/ACCESS.2020.3005823
Mehta, A., & Kumar, R. (2021). Enhancing cybersecurity in emerging economies: A case study of India’s response strategies. Information & Computer Security, 29(2), 213–229. https://doi.org/10.1108/ICS-09-2020-0122
Munyua, W., & Wanjiku, S. (2020). Cybersecurity capacity development in Africa: Bridging gaps and building resilience. Information Technology for Development, 26(4), 768–786. https://doi.org/10.1080/02681102.2020.1774667
Nguyen, T. H., Ngo, L. V., & Ruël, H. (2022). A TOE framework perspective of smart technology adoption in banking. Technological Forecasting and Social Change, 176, 121464. https://doi.org/10.1016/j.techfore.2022.121464
Shao, Z., Zhang, L., & Li, X. (2021). Information processing capability and organizational performance in the digital era: The role of IT capability. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2020.103434
Sharma, A., & Gupta, B. B. (2021). AI-based intrusion detection systems: A review of ML and DL approaches. Journal of Information Security and Applications, 58, 102804. https://doi.org/10.1016/j.jisa.2021.102804
Zhou, C., Han, Z., & Wang, Q. (2021). Deep learning-based anomaly detection in cybersecurity: A comprehensive review. Computers & Security, 106, 102271. https://doi.org/10.1016/j.cose.2021.102271
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Yuki Nakamura
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
