Enhancing Threat Detection in Financial Cyber Security Through Auto Encoder-MLP Hybrid Models

Cyber-attacks have the potential to cause power outages, malfunctions with military equipment, and breaches of sensitive data. Owing to the substantial financial value of the information it contains, the banking sector is especially vulnerable. The number of digital footprints that banks have increases, increasing the attack surface available to hackers. This paper presents a unique approach to improve financial cyber security threat detection by integrating Auto Encoder-Multilayer Perceptron (AE-MLP) hybrid models. These models use MLP neural networks' discriminative capabilities for detection tasks, while also utilizing auto encoders' strengths in collecting complex patterns and abnormalities in financial data. The NSL-KDD dataset, which is varied and includes transaction records, user activity patterns, and network traffic, was thoroughly analysed. The results show that the AE-MLP hybrid models perform well in spotting possible risks including fraud, data breaches, and unauthorized access attempts. Auto encoders improve the accuracy of threat detection methods by efficiently compressing and rebuilding complicated data representations. This makes it easier to extract latent characteristics that are essential for differentiating between normal and abnormal activity. The approach is implemented with Python software. The recommended Hybrid AE+MLP approach shows better accuracy with 99%, which is 13.16% more sophisticated, when compared to traditional approach. The suggested approach improves financial cyber security systems' capacity for prediction while also providing scalability and efficiency while handling massive amounts of data in real-time settings.

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