Attention-fused recurrent neural networks for DDoS attack detection based on blockchain technology

Abstract

With the advent of the "Internet +" era, network viruses, hacker attacks, and personal data breaches have gradually become the cybersecurity issues that people are concerned about. Blockchain technology has the advantages such as decentralization, immutability, and smart contracts. It can be used in network infrastructure protection to prevent computers from being hacked illegally, ensuring the integrity and security of users' personal data. In this paper, we propose a DDoS attack detection method based on the attention mechanism of LSTM that involves using a deep learning model to analyze network traffic data and identify DDoS attacks, named A-LSTM. The attention mechanism in LSTM helps to focus on the most important features in the data, which are critical for accurate DDoS attack detection. This method has the potential to improve the accuracy of DDoS attack detection compared to traditional methods by leveraging the powerful pattern recognition capabilities of deep learning models.