On the public citation graph
FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning
0 signed · 72 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-47426-3_27openalex-snapshot
Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters
inferredcites →https://doi.org/10.1007/978-3-030-90196-7_1openalex-snapshot
Federated Learning Based Distributed Algorithms for RF Fingerprinting Extraction and Identification of IoT Devices
inferredcites →https://doi.org/10.1007/978-3-642-41822-8_15openalex-snapshot
Auto-encoder Based Data Clustering
inferredcites →https://doi.org/10.1016/j.comnet.2018.11.013openalex-snapshot
Towards automatic fingerprinting of IoT devices in the cyberspace
inferredcites →https://doi.org/10.1016/j.iot.2022.100514openalex-snapshot
AI for next generation computing: Emerging trends and future directions
inferredcites →https://doi.org/10.1109/ccem50674.2020.00021openalex-snapshot
Federated K-Means Clustering: A Novel Edge AI Based Approach for Privacy Preservation
inferredcites →https://doi.org/10.1109/ccwc54503.2022.9720739openalex-snapshot
Smart Recon: Network Traffic Fingerprinting for IoT Device Identification
inferredcites →https://doi.org/10.1109/cns.2019.8802783openalex-snapshot
Design of a Robust RF Fingerprint Generation and Classification Scheme for Practical Device Identification
inferredcites →https://doi.org/10.1109/icdcs.2017.283openalex-snapshot
IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT
inferredcites →https://doi.org/10.1109/infocom.2019.8737455openalex-snapshot
Real-time Identification of Rogue WiFi Connections Using Environment-Independent Physical Features
inferredcites →https://doi.org/10.1109/infocom.2019.8737463openalex-snapshot
ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
inferredcites →https://doi.org/10.1109/jiot.2019.2946500openalex-snapshot
Transforming Device Fingerprinting for Wireless Security via Online Multitask Metric Learning
inferredcites →https://doi.org/10.1109/jiot.2021.3128646openalex-snapshot
Data Poisoning Attacks on Federated Machine Learning
inferredcites →https://doi.org/10.1109/jsac.2019.2904364openalex-snapshot
AuDI: Toward Autonomous IoT Device-Type Identification Using Periodic Communication
inferredcites →https://doi.org/10.1109/massw.2019.00009openalex-snapshot
Machine Learning for Security at the IoT Edge - A Feasibility Study
inferredcites →https://doi.org/10.1109/mnet.001.1900506openalex-snapshot
On Safeguarding Privacy and Security in the Framework of Federated Learning
inferredcites →https://doi.org/10.1109/mprv.2018.03367731openalex-snapshot
N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
inferredcites →https://doi.org/10.1109/rteict.2017.8256809openalex-snapshot
An ANN based approach for wireless device fingerprinting
inferredcites →https://doi.org/10.1109/tmc.2018.2866249openalex-snapshot
Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00023openalex-snapshot
IoT Device Identification via Network-Flow Based Fingerprinting and Learning
inferredcites →https://doi.org/10.1109/wcnc.2019.8885429openalex-snapshot
IoT Device Fingerprinting: Machine Learning based Encrypted Traffic Analysis
inferredcites →https://doi.org/10.1145/3133956.3133982openalex-snapshot
Practical Secure Aggregation for Privacy-Preserving Machine Learning
inferredcites →https://doi.org/10.1145/3266444.3266452openalex-snapshot
Behavioral Fingerprinting of IoT Devices
inferredcites →https://doi.org/10.1145/3319535.3339810openalex-snapshot
DeMiCPU: Device Fingerprinting with Magnetic Signals Radiated by CPU
inferredcites →https://doi.org/10.1145/3319535.3354240openalex-snapshot
Automatic Fingerprinting of Vulnerable BLE IoT Devices with Static UUIDs from Mobile Apps
inferredcites →https://doi.org/10.1515/popets-2015-0027openalex-snapshot
Fingerprinting Mobile Devices Using Personalized Configurations
inferredcites →https://doi.org/10.48550/arxiv.1511.03575openalex-snapshot
Federated Optimization:Distributed Optimization Beyond the Datacenter
inferredcites →https://doi.org/10.48550/arxiv.1709.04647openalex-snapshot
Detection of Unauthorized IoT Devices Using Machine Learning Techniques
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.70237openalex-snapshot
ConFedDI : Efficient IoT Device Identification via Horizontal Federated Learning and Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1002/spy2.355openalex-snapshot
Botnet‐based IoT network traffic analysis using deep learning
inferred← citeshttps://doi.org/10.1007/978-3-031-57537-2_7openalex-snapshot
A Small World–Privacy Preserving IoT Device-Type Fingerprinting with Small Datasets
inferred← citeshttps://doi.org/10.1007/978-3-032-27702-2_27openalex-snapshot
Traffic Fingerprinting of Encrypted IoT Protocols and Privacy Mitigations
inferred← citeshttps://doi.org/10.1007/978-3-032-33260-8_18openalex-snapshot
“Room for More?": Behavior Adaptive IoT Device-type Fingerprinting
inferred← citeshttps://doi.org/10.1007/978-981-96-2417-1_23openalex-snapshot
Integrating CP-ABE and Device Fingerprint Into Federated Learning
inferred← citeshttps://doi.org/10.1007/s00607-025-01572-2openalex-snapshot
Federated learning for heart disease detection and classification in edge enabled IoMT-based healthcare: taxonomy, challenges, and opportunities
inferred← citeshttps://doi.org/10.1007/s10489-025-06639-3openalex-snapshot
GTIBS: secure smart home monitoring through gateway traffic analysis and behavioral signature identification
inferred← citeshttps://doi.org/10.1007/s10586-025-05905-wopenalex-snapshot
Fair client selection and encrypted aggregation: a federated learning framework for intrusion detection in resource-constrained networks
inferred← citeshttps://doi.org/10.1016/j.comcom.2025.108149openalex-snapshot
LFIoTDI: A lightweight and fine-grained device identification approach for IoT security enhancement
inferred← citeshttps://doi.org/10.1016/j.comnet.2026.112040openalex-snapshot
FedHome: A federated learning framework for smart home device classification and attack detection by broadband service providers
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110515openalex-snapshot
Open problems and challenges in federated learning for IoT: A comprehensive review and strategic guide
inferred← citeshttps://doi.org/10.1016/j.future.2024.05.025openalex-snapshot
mWIoTAuth: Multi-wearable data-driven implicit IoT authentication
inferred← citeshttps://doi.org/10.1016/j.iot.2025.101758openalex-snapshot
A comprehensive review of IoT device fingerprinting: Insights into techniques, trends, challenges, and future directions
inferred← citeshttps://doi.org/10.1016/j.iot.2025.101785openalex-snapshot
FGLIoT: IoT device identification via federated graph learning and spatio-temporal feature fusion
inferred← citeshttps://doi.org/10.1016/j.iot.2025.101802openalex-snapshot
iPASecIoT: An intelligent pipeline for automatic and adaptive feature extraction for secure IoT device identification and intrusion detection
inferred← citeshttps://doi.org/10.1051/itmconf/20257804003openalex-snapshot
Privacy Protection Optimization in Federated Learning
inferred← citeshttps://doi.org/10.1093/comjnl/bxaf101openalex-snapshot
A mutual detection method for wireless IoT devices based on encrypted traffic analysis
inferred← citeshttps://doi.org/10.1109/access.2024.3454074openalex-snapshot
HAFedL: A Hessian-Aware Adaptive Privacy Preserving Horizontal Federated Learning Scheme for IoT Applications
inferred← citeshttps://doi.org/10.1109/access.2025.3611160openalex-snapshot
AI Versus IoT Security: Fingerprinting and Defenses Against TLS Handshake-Based IoT Device Classification
inferred← citeshttps://doi.org/10.1109/acsac67867.2025.00070openalex-snapshot
A Period-Adaptive Traffic Fingerprint-Based Method for Smart Home Device Identification
inferred← citeshttps://doi.org/10.1109/ccai69603.2026.11642061openalex-snapshot
Enhancing Encrypted IoT Traffic Classification with Directional-Context and Masked Autoencoder
inferred← citeshttps://doi.org/10.1109/comsnets67989.2026.11418087openalex-snapshot
FedTID: A Taxonomy Aware Federated Intrusion Detection Framework for non-IID IoT Environment
inferred← citeshttps://doi.org/10.1109/ic2pct68894.2026.11584447openalex-snapshot
Privacy Preserving Multimodal Biometric Authentication Through Federated Cross Modal Learning
inferred← citeshttps://doi.org/10.1109/icaiccit68829.2025.11434029openalex-snapshot
A Deep Dive into IOT Device Security: Threats and Countermeasures
inferred← citeshttps://doi.org/10.1109/icc59461.2026.11586863openalex-snapshot
Fingerprinting AI Applications and Phases in Edge-Assisted Distributed Learning and Inference using Side-Channel Data
inferred← citeshttps://doi.org/10.1109/icisct67740.2026.11576468openalex-snapshot
A Survey on Machine Learning-Based IoT Classification and Fingerprinting
inferred← citeshttps://doi.org/10.1109/icspcs63175.2024.10815844openalex-snapshot
An Improved IoT Device Fingerprinting via Enhanced Feature Selection
inferred← citeshttps://doi.org/10.1109/jiot.2026.3664710openalex-snapshot
DFGLIoT: A Dual-Fusion Graph Learning Framework for Cross-Institutional Mobile IoT Device Identification
inferred← citeshttps://doi.org/10.1109/tifs.2026.3723076openalex-snapshot
MsaaDI: A Heterogeneity-Resilient Federated Learning Framework for IoT Device Identification With Multi-Scale Adaptive Aggregation
inferred← citeshttps://doi.org/10.1109/tii.2025.3534441openalex-snapshot
Network Traffic Fingerprinting for IIoT Device Identification: A Survey
inferred← citeshttps://doi.org/10.1109/tits.2025.3525505openalex-snapshot
Explainable Deep Learning-Enabled Malware Attack Detection for IoT-Enabled Intelligent Transportation Systems
inferred← citeshttps://doi.org/10.1109/tmc.2025.3581534openalex-snapshot
Hierarchical and Heterogeneous Federated Learning via a Learning-on-Model Paradigm
inferred← citeshttps://doi.org/10.1109/tnet.2024.3395278openalex-snapshot
PoirIoT: Fingerprinting IoT Devices at Tbps Scale
inferred← citeshttps://doi.org/10.1109/tnsm.2025.3616966openalex-snapshot
SLIoTDI: Scalable and Lightweight IoT Device Identification With Session-Level Grayscale Fingerprinting and Adversarial Training
inferred← citeshttps://doi.org/10.1109/ton.2026.3668183openalex-snapshot
GeoFL: A Framework for Efficient Geo-Distributed Cross-Device Federated Learning
inferred← citeshttps://doi.org/10.1109/tps-isa67132.2025.00022openalex-snapshot
It's About Time!: Exploiting Timing Variance for IoT Device-Type Fingerprinting
inferred← citeshttps://doi.org/10.1145/3722232openalex-snapshot
PrivacyCube: Data Physicalization for Enhancing Privacy Awareness in IoT
inferred← citeshttps://doi.org/10.3390/electronics14163267openalex-snapshot
Asset Discovery in Critical Infrastructures: An LLM-Based Approach
inferred← citeshttps://doi.org/10.3390/s24051482openalex-snapshot
Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.