# Utilizing Deep Learning for Enhancing Network Resilience in Finance

arXiv: https://arxiv.org/abs/2402.09820 · HTML: https://mlsanity.com/paper/2402.09820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms](https://mlsanity.com/paper/2312.12872/) (2023)
- [Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method](https://mlsanity.com/paper/2401.06782/) (2024)
- [Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling](https://mlsanity.com/paper/2401.05433/) (2024)

## Built on by (most cited first, 1 of 1)

- [The Role of AI in Financial Forecasting: ChatGPT's Potential and Challenges](https://mlsanity.com/paper/2411.13562/) (2024)

## Abstract (arXiv)

In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is an important part of a complete and effective defense system. How to effectively detect unknown threats is one of the concerns of network protection. Currently, network threat detection is usually based on rules and traditional machine learning methods, which create artificial rules or extract common spatiotemporal features, which cannot be applied to large-scale data applications, and the emergence of unknown risks causes the detection accuracy of the original model to decline. With this in mind, this paper uses deep learning for advanced threat detection to improve protective measures in the financial industry. Many network researchers have shifted their focus to exception-based intrusion detection techniques. The detection technology mainly uses statistical machine learning methods - collecting normal program and network behavior data, extracting multidimensional features, and training decision machine learning models on this basis (commonly used include naive Bayes, decision trees, support vector machines, random forests, etc.).