Work

FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning

Year: 2025

Type: article

Source: IEEE Transactions on Dependable and Secure Computing

DOI: 10.1109/tdsc.2025.3544106

Authors: Ning Wang, Shanghao Shi, Yimin Chen, Wenjing Lou, Y. Thomas Hou

Institutions: University of South Florida, Virginia Tech, University of Massachusetts Lowell

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: Commonwealth Cyber Initiative, National Science Foundation, Office of Naval Research

Awards: EAGER: Toward a Decentralized Cross-administrator Zone Management System: Policy and Technology, G2454284058, G3405165046, CPS: Medium: Collaborative Research: Robust Sensing and Learning for Autonomous Driving Against Perceptual Illusion, Collaborative Research: SaTC: CORE: Medium: An Anti-tracking and Robocall-free Architecture for Next-G Mobile Networks, Collaborative Research: NeTS: Medium: An Integrated Multi-Time Scale Approach to High-Performance, Intelligent, and Secure O-RAN based NextG, Collaborative Research: SaTC: CORE: Medium: A Networking Perspective of Blockchain Security: Modeling, Analysis, and Defense, SWIFT-SAT: Efficient and On-Demand Spectrum Coexistence for Satellite-Terrestrial Systems

OpenAlex ID: W4407782069

FWCI: 10.88

Cites: 59

Cited by: 25

Related to: 10