Quantum Federated Learning for Privacy-Preserving Anomaly Detection

SecureFedShield: An Adaptive Privacy-Preserving Federated Defense Framework Against Adversarial Attacks in Financial Fraud Detection

SecureFedShield is proposed, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments that integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture.

Adaptive Intrusion Detection in WSNs Using Federated Deep Learning and Homomorphic Encryption

This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy, and offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.

SecureEnsembleNet: Intelligent Cyber Threat Detection with Ensemble Learning

The rapid growth of network-connected systems has made cyber threat detection a critical priority for modern infrastructures. Traditional signature-based intrusion detection systems (IDSs) struggle to detect novel and evolving attacks, creating the need for intelligent learning-based approaches. This paper presents Sec...

A Secure Federated Learning and Blockchain Framework for E-Health Threat Detection

The proposed framework effectively integrates encryption, federated intrusion detection, explainable artificial intelligence, and blockchain security to enhance privacy, transparency, and reliability in IoMT healthcare networks.

Privacy-Preserving Intrusion Detection using Federated Learning in Distributed Networks

The rapid growth of things like distributed computing, cloud platforms, edge setups, and all kinds of IoT devices have greatly disrupted how our networks work. But while these developments bring some progress, they have also made cyber attacks more frequent and sophisticated. Conventional tools for detecting these prob...

Privacy-Preserving Intrusion Detection Using Federated Learning in Distributed Networks