Applying the defense model to strengthen information security with artificial intelligence in computer networks of the financial services sector.

Journal: Scientific reports
Published Date: Aug 19, 2025

Abstract

The increasing digitization of the Financial Services Sector (FSS) has significantly improved operational efficiency but has also exposed institutions to sophisticated Cyber Threat Intelligence (CTI) such as Advanced Persistent Threats (APT), zero-day exploits, and high-volume Denial-of-Service (DoS) attacks. Traditional Intrusion Detection Systems (IDS), including signature-based and anomaly-based approaches, suffer from high False Positive Rates (FPR) and lack the adaptability required for modern threat landscapes. This study aims to develop and evaluate an Artificial Intelligence-Enhanced Defense-in-Depth (AI-E-DiD) designed to provide real-time, adaptive, and scalable cybersecurity prevention for financial networks. The proposed model integrates a hybrid Generative Adversarial Network and Long Short-Term Memory Autoencoder (GAN-LSTM-AE) for intelligent anomaly detection, an Advanced Encryption Standard in Galois/Counter Mode (AES-GCM) for data integrity and confidentiality, and an AI-Enhanced Intrusion Prevention System (AI-E-IPS) for dynamic threat mitigation. Empirical evaluation using the NSL-KDD and CICIDS-2017 datasets demonstrates high detection accuracy (95.6% for DoS and 94.2% for DDoS), low response times (< 0.25 s), and robust performance under varying user loads, attack types, and data sizes. The NS-3 results show that AI-DiD outperforms conventional IDS and traditional DiD in terms of Detection Rate (DR), Computational Overhead (CO), Network Throughput (NT), and operational scalability. These findings highlight the model's probable for deployment in high-stakes financial environments requiring resilient and intelligent cybersecurity infrastructure.

Authors

  • Arodh Lal Karn
    Department of Financial and Actuarial Mathematics, School of Mathematics and Physics, Xian Jiaotong-Liverpool University, Suzhou City, Jiangsu Province, 215123, P.R. China.
  • Hayder M A Ghanimi
    Department of Information Technology, College of Science, University of Warith Al- Anbiyaa, Karbala, 56001, Iraq.
  • Vijayalakshmi Iyengar
    Department of Management, SRM University, Delhi-NCR-Ghaziabad Campus, New Delhi, 201204, India.
  • Mohd Shuaib Siddiqui
    Department of Management, Faculty of Business Administration, University of Tabuk, Tabuk, 71491, Saudi Arabia.
  • Meshal Ghalib Alharbi
    Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia.
  • Roobaea Alroobaea
    Department Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
  • Amr Yousef
    Electrical Engineering Department, University of Business and Technology, Jeddah, Saudi Arabia.
  • Sudhakar Sengan
    Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, India.

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