Tides of Blockchain in IoT Cybersecurity

Sensors, Journal Year: 2024, Volume and Issue: 24(10), P. 3111 - 3111

Published: May 14, 2024

This paper surveys the implementation of blockchain technology in cybersecurity Internet Things (IoT) networks, presenting a comprehensive framework that integrates with intrusion detection systems (IDS) to enhance IDS performance. reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), identify emerging trends challenges this field. An analysis approaches incorporating AI demonstrates potentiality integrating transform IDS. paper’s structure establishes foundation for further investigation provides blueprint development is accessible, scalable, transparent, immutable, decentralized. A demonstration case studies shows viability combining duo Despite posed by resource constraints privacy concerns, it notable key securing networks continued innovation area necessary. Further research into lightweight cryptography, efficient consensus mechanisms, privacy-preserving techniques needed realize all potential blockchain-powered IoT.

Language: Английский

Federated Learning for Privacy-Preserving Cybersecurity: A Review on Secure Threat Detection

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 520 - 528

Published: April 12, 2025

Federated Learning's (FL) distributed threat detection technique is a significant advancement in cybersecurity as it preserves privacy while processing data decentralized manner. Centralized security systems that rely on raw collection present two major threats to users because they create regulatory problems addition breaches. FL removes concerns through its model-building process, allowing different organizations work together without sharing private data. This document investigates FL's role an analysis of malware/ransomware detection, IDS applications, secure and network traffic anomaly detection. The paper explores effective privacy-protecting techniques: implementations are protected against Byzantine backdoor attacks using Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), Differential Privacy (DP), Model Aggregation. delivers advantages but encounters challenges mainly related excessive communication demands well performance deterioration under adversarial conditions, difficulties with system expansion. research provides exhaustive FL-based frameworks discussing existing applications future developments for these the need advanced methods improve dependability solutions.

Language: Английский

Secure data transmission in cloud computing using a cyber-security trust model with multi-risk protection scheme in smart IOT application

Cluster Computing, Journal Year: 2024, Volume and Issue: 28(2)

Published: Nov. 26, 2024

Language: Английский