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VOL. 12, ISSUE 1 (2026)
AI-Powered intrusion detection systems for banking networks: evidence from Indian digital banking infrastructure
Authors
Dr. Akhilesh Kumar
Abstract
The rapid growth of digital banking services
in India has significantly increased the exposure of financial institutions to
sophisticated cyber threats. Traditional security mechanisms are often
insufficient to detect complex and evolving intrusion patterns in modern
banking networks. This study explores the role of Artificial Intelligence
(AI)-powered Intrusion Detection Systems (IDS) in strengthening the security of
Indian digital banking infrastructure. The research analyzes how machine
learning and deep learning techniques can identify malicious activities,
abnormal traffic patterns, and potential cyberattacks in real time. Using
network traffic datasets and simulated banking transaction environments,
various AI-based models such as Random Forest, Support Vector Machine, and
Neural Networks are evaluated for their effectiveness in detecting intrusions.
The results demonstrate that AI-driven IDS significantly improves detection
accuracy, reduces false positives, and enables proactive threat mitigation compared
to conventional rule-based systems. The findings highlight the importance of
integrating intelligent security frameworks within banking networks to protect
sensitive financial data and ensure the reliability of digital payment
ecosystems in India. This study contributes to the growing body of research on
AI-based cybersecurity solutions and provides practical insights for financial
institutions seeking to enhance their cyber defense strategies.
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Pages:299-306
How to cite this article:
Dr. Akhilesh Kumar "AI-Powered intrusion detection systems for banking networks: evidence from Indian digital banking infrastructure". International Journal of Commerce and Management Research, Vol 12, Issue 1, 2026, Pages 299-306
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