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

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.