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.
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.
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...
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.
A privacy-friendly intrusion detection system (PP-IDS) that operates on the basis of conjoint learning, which makes PP-IDS suitable for cloud services, edge devices, Internet of Things networks or business systems – basically anywhere that data needs to remain private.
Evaluated on standard intrusion detection datasets, including NSL-KDD and CICIDS2017, indicates that the proposed model can maintain competitive detection performance while improving privacy protection and supporting deployment across distributed network environments.
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