Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks

2024-09-08 · Tran Viet Khoa, Mohammad Abu Alsheikh, Yibeltal Alem, Dinh Thai Hoang

This paper presents a novel Collaborative Cyberattack Detection (CCD) system aimed at enhancing the security of blockchain-based data-sharing networks by addressing the complex challenges associated with noise addition in federated learning models. Leveraging the theoretical principles of differential privacy, our approach strategically integrates noise into trained sub-models before reconstructing the global model through transmission. We systematically explore the effects of various noise types, i.e., Gaussian, Laplace, and Moment Accountant, on key performance metrics, including attack detection accuracy, deep learning model convergence time, and the overall runtime of global model generation. Our findings reveal the intricate trade-offs between ensuring data privacy and maintaining system performance, offering valuable insights into optimizing these parameters for diverse CCD environments. Through extensive simulations, we provide actionable recommendations for achieving an optimal balance between data protection and system efficiency, contributing to the advancement of secure and reliable blockchain networks.

📄 PDF Abstract BibTeX arXiv:2409.04972

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learning

FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination

2023-04-07 · Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, Nasser Hosseinzadeh 외

With the growing concern about the security and privacy of smart grid systems, cyberattacks on critical power grid components, such as state estimation, have proven to be one of the top-priority cyber-related issues and …

Federated Learning Privacy Preserving Quantization Representation Learning+1

Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks

2023-06-14 · Zhe Wang, Tingkai Yan

With the development of the Internet of Vehicles (IoV), vehicle wireless communication poses serious cybersecurity challenges. Faulty information, such as fake vehicle positions and speeds sent by surrounding vehicles, c…

Federated Learning Prediction Trajectory Prediction

An Interpretable Federated Learning-based Network Intrusion Detection Framework

2022-01-10 · Tian Dong, Song Li, Han Qiu, Jialiang Lu

Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks. Existing learning-based NIDS mainly uses Neural Network (NN) as a classifier that relies on the quality a…

Federated Learning Intrusion Detection Network Intrusion Detection

FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination

2023-03-28 · Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, Nasser Hosseizadeh 외

With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years. To remedy the challenges of privacy preservation and …

Federated Learning Intrusion Detection Privacy Preserving

Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM

2025-02-25 · Yuqing Wang, Xiao Yang

Traditional security protection methods struggle to address sophisticated attack vectors in large-scale distributed systems, particularly when balancing detection accuracy with data privacy concerns. This paper presents …

Computational Efficiency Federated Learning