Detecting Data Poisoning Attacks using Federated Learning with Deep Neural Networks: An Empirical Study

Abstract

The advent of intelligent networks powered by machine learning (ML) methods over the past few years has dramatically facilitated various facets of human lives, including healthcare, transportation, and entertainment. However, the use of ML in intelligent networks raises serious concerns about privacy and security, particularly in the context of data poisoning attacks. In order to address these concerns, this research paper presents a novel technique for detecting data poisoning attacks in intelligent networks, focusing on addressing privacy and security concerns associated with the use of machine learning (ML) methods. The research combines federated learning and deep learning approaches to analyze network data in a distributed and privacy-preserving manner. The technique employs a federated neural network to identify malicious data by analyzing network traffic, leveraging the power of Bayesian convolutional neural networks for efficient and accurate detection. The research follows an empirical approach, conducting experimental analyses to evaluate the proposed technique's effectiveness in terms of network security and data classification. The results demonstrate significant performance, including high throughput, quality of service, transmission rate, and low root mean square error for network security. Furthermore, the technique achieves impressive accuracy, recall, precision and malicious data analysis for data detection. The findings of this research contribute to enhancing the security and integrity of intelligent networks, benefiting various stakeholders, including network administrators, data privacy advocates, and users relying on secure network communication.