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AI-Driven Real-Time Cyberattack Detection for Critical Financial Infrastructure

Lalitha Reddy Badam

Independent Researcher, University of New Haven, USA

Abstract

The heightened digitalization of financial services has grown the target landscape of critical financial infrastructure to great heights so that a real-time cyberattack detection is the only tool that adds to the assurance that the system is safe, operational, and the customers trust it. In this article, a plan of an AI-based Real-Time Cyberattack Detection and Financial Resilience Framework (AI-RTCDF), which combines AI, cybersecurity, and financial-system resiliency to identify, evaluate and react to advanced cyber-attacks in a changing financial landscape is presented. The framework proposed will unify real-time ingestion of data, network ingestion of data, preprocessing, feature engineering, threat detection using artificial intelligences, behavior analysis, risk scoring, auto-response, and continuous monitoring of resilience. The methods to detect known and previously unknown patterns of attack are machine learning and deep learning and an adaptive layer of intelligence is continually updated so it detects models based on new behavior of threats. Death risk response mechanism will take the form of prioritizing identified cases by their ability to impact on financial operations, and recover and contain such cases within a short time. The additional resilience indicators in the framework that measure overall strength of financial infrastructure include availability of the services, the possibility of recovering, integrity of transaction and analysis of response. The proposed approach provides a unified route to between real-time threat identification and intelligent response and resiliency analysis, which helps to actively monitor cybersecurity and reduce the operational and economic impact of the cyberattacks.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2023.0604011

Pages

10374-10383

Published

August 16, 2023
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Lalitha Reddy Badam (%2023). AI-Driven Real-Time Cyberattack Detection for Critical Financial Infrastructure. International Journal of Science, Research and Technology , Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 10374-10383. https://doi.org/10.15662/IJSRAT.2023.0604011

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