On the public citation graph

https://doi.org/10.2139/ssrn.4172619

0 signed · 34 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.

1 of the 33 references listed on this page is in the Retraction Watch database as retracted (export 2026-10-21): 10.1155/2022/5448647 (notice 2024-01-09).

Dataset join, best-effort; this page does not check the publisher record. Retraction Watch data via Crossref Labs, used with credit; OpenCitations Index (CC0) and Meta (CC BY 4.0); dataset doi:10.7910/DVN/WT8VOA (CC BY 4.0).

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-71017-0_23openalex-extracted
Analysis of Conpot and Its BACnet Features for Cyber-Deception
inferredcites →https://doi.org/10.1007/978-981-13-1810-8_37openalex-extracted
Extreme Gradient Boosting Based Tuning for Classification in Intrusion Detection Systems
inferredcites →https://doi.org/10.1016/j.comcom.2021.09.029openalex-extracted
AI-empowered, blockchain and SDN integrated security architecture for IoT network of cyber physical systems
inferredcites →https://doi.org/10.1016/j.comnet.2020.107183openalex-extracted
A NSGA2-LR wrapper approach for feature selection in network intrusion detection
inferredcites →https://doi.org/10.1016/j.comnet.2020.107247openalex-extracted
Building an efficient intrusion detection system based on feature selection and ensemble classifier
inferredcites →https://doi.org/10.1016/j.comnet.2020.107315openalex-extracted
An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset
inferredcites →https://doi.org/10.1016/j.comnet.2020.107784openalex-extracted
Towards a deep learning-driven intrusion detection approach for Internet of Things
inferredcites →https://doi.org/10.1016/j.comnet.2021.108498openalex-extracted
A generalized machine learning model for DDoS attacks detection using hybrid feature selection and hyperparameter tuning
inferredcites →https://doi.org/10.1016/j.comnet.2021.108658openalex-extracted
MFFusion: A Multi-level Features Fusion Model for Malicious Traffic Detection based on Deep Learning
inferredcites →https://doi.org/10.1016/j.comnet.2022.108818openalex-extracted
SecFHome: Secure remote authentication in fog-enabled smart home environment
inferredcites →https://doi.org/10.1016/j.cose.2019.04.011openalex-extracted
Context aware intrusion detection for building automation systems
inferredcites →https://doi.org/10.1016/j.cose.2021.102527openalex-extracted
Security of Building Automation and Control Systems: Survey and future research directions
inferredcites →https://doi.org/10.1016/j.future.2019.02.064openalex-extracted
Designing collaborative blockchained signature-based intrusion detection in IoT environments
inferredcites →https://doi.org/10.1016/j.future.2021.03.024openalex-extracted
Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection
inferredcites →https://doi.org/10.1016/j.ijcip.2022.100508openalex-extracted
FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid
inferredcites →https://doi.org/10.1016/j.jnca.2020.102942openalex-extracted
A GRU deep learning system against attacks in software defined networks
inferredcites →https://doi.org/10.1016/j.sysarc.2021.102048openalex-extracted
A global-energy-aware virtual machine placement strategy for cloud data centers
inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-extracted
A detailed analysis of the KDD CUP 99 data set
inferredcites →https://doi.org/10.1109/compsac51774.2021.00075openalex-extracted
Ensemble-based Efficient Anomaly Detection for Smart Building Control Systems
inferredcites →https://doi.org/10.1109/comst.2019.2962586openalex-extracted
A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues
inferredcites →https://doi.org/10.1109/jiot.2019.2926365openalex-extracted
A Supervised Intrusion Detection System for Smart Home IoT Devices
inferredcites →https://doi.org/10.1109/jiot.2021.3084796openalex-extracted
MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles
inferredcites →https://doi.org/10.1109/mie.2021.3120235openalex-extracted
Data-Driven Edge Computing: A Fabric for Intelligent Building Energy Management Systems
inferredcites →https://doi.org/10.1109/tdsc.2020.2973992openalex-extracted
On High-Speed Flow-Based Intrusion Detection Using Snort-Compatible Signatures
inferredcites →https://doi.org/10.1109/tem.2019.2922936openalex-extracted
DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids
inferredcites →https://doi.org/10.1109/tie.2010.2040562openalex-extracted
Implementation of a Control Loop Experiment in a Network-Based Control System With LonWorks Technology and IP Networks
inferredcites →https://doi.org/10.1109/tii.2011.2166792openalex-extracted
Semantic-Based Enhancement of ISO/IEC 14543-3 EIB/KNX Standard for Building Automation
inferredcites →https://doi.org/10.1109/tnsm.2020.2971776openalex-extracted
Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection
inferredcites →https://doi.org/10.1145/2947626.2951964openalex-extracted
A Signature-Based Intrusion Detection System for Web Applications based on Genetic Algorithm
inferredcites →https://doi.org/10.1145/3406115openalex-extracted
AI-empowered IoT Security for Smart Cities
inferredcites →https://doi.org/10.1155/2022/5448647openalex-extracted
Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM
inferredcites →https://doi.org/10.3390/s20113048openalex-extracted
Teaching and Learning IoT Cybersecurity and Vulnerability Assessment with Shodan through Practical Use Cases
inferredcites →https://doi.org/10.5220/0006639801080116openalex-extracted
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1109/jiot.2024.3404808openalex-snapshot
IoTSecUT: Uncertainty-Based Hybrid Deep Learning Approach for Superior IoT Security Amidst Evolving Cyber Threats
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.