On the public citation graph
Efficient Real-Time Anomaly Detection in IoT Networks Using One-Class Autoencoder and Deep Neural Network
0 signed · 77 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known work and none has been retracted — and get a dated CiteStamped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/10.1007/978-1-4842-4470-8openalex-snapshot
Building Machine Learning and Deep Learning Models on Google Cloud Platform
inferredcites →https://doi.org/10.1007/978-3-030-95630-1_4openalex-snapshot
A Deep Learning-Based Intrusion Detection Technique for a Secured IoMT System
inferredcites →https://doi.org/10.1007/978-981-15-1451-7_11openalex-snapshot
Intrusion Detection Systems (IDS)—An Overview with a Generalized Framework
inferredcites →https://doi.org/10.1007/978-981-15-3075-3_6openalex-snapshot
Enhancing IoT Botnets Attack Detection Using Machine Learning-IDS and Ensemble Data Preprocessing Technique
inferredcites →https://doi.org/10.1007/978-981-16-7610-9_11openalex-snapshot
Intrusion Detection System Intensive on Securing IoT Networking Environment Based on Machine Learning Strategy
inferredcites →https://doi.org/10.1007/s00521-015-1939-3openalex-snapshot
Learning a good representation with unsymmetrical auto-encoder
inferredcites →https://doi.org/10.1007/s10489-018-01405-0openalex-snapshot
RAMD: registry-based anomaly malware detection using one-class ensemble classifiers
inferredcites →https://doi.org/10.1007/s10489-021-02205-9openalex-snapshot
Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM
inferredcites →https://doi.org/10.1007/s10586-022-03776-zopenalex-snapshot
Internet of Things intrusion detection systems: a comprehensive review and future directions
inferredcites →https://doi.org/10.1007/s11760-024-03248-4openalex-snapshot
Customized convolutional neural network model for IoT botnet attack detection
inferredcites →https://doi.org/10.1007/s11831-020-09496-0openalex-snapshot
A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges
inferredcites →https://doi.org/10.1007/s12083-024-01712-zopenalex-snapshot
Enhancing cybersecurity in IoT networks: SLSTM-WCO algorithm for anomaly detection
inferredcites →https://doi.org/10.1007/s12652-022-04350-6openalex-snapshot
Hybrid intrusion detection model based on a designed autoencoder
inferredcites →https://doi.org/10.1007/s42979-021-00516-9openalex-snapshot
Intrusion Detection System for IOT Botnet Attacks Using Deep Learning
inferredcites →https://doi.org/10.1016/j.aci.2018.08.003openalex-snapshot
Classification assessment methods
inferredcites →https://doi.org/10.1016/j.comnet.2021.108658openalex-snapshot
MFFusion: A Multi-level Features Fusion Model for Malicious Traffic Detection based on Deep Learning
inferredcites →https://doi.org/10.1016/j.compeleceng.2022.107810openalex-snapshot
Anomaly-based intrusion detection system for IoT networks through deep learning model
inferredcites →https://doi.org/10.1016/j.cosrev.2024.100665openalex-snapshot
Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives
inferredcites →https://doi.org/10.1016/j.csi.2023.103829openalex-snapshot
MRFM: A timely detection method for DDoS attacks in IoT with multidimensional reconstruction and function mapping
inferredcites →https://doi.org/10.1016/j.eswa.2022.119330openalex-snapshot
Implementation of intrusion detection model for DDoS attacks in Lightweight IoT Networks
inferredcites →https://doi.org/10.1016/j.future.2019.05.041openalex-snapshot
Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset
inferredcites →https://doi.org/10.1016/j.ijinfomgt.2021.102442openalex-snapshot
Internet of Things (IoT): From awareness to continued use
inferredcites →https://doi.org/10.1016/j.iot.2023.100699openalex-snapshot
A deep learning approach for intrusion detection in Internet of Things using focal loss function
inferredcites →https://doi.org/10.1016/j.iot.2023.100936openalex-snapshot
Enhancing IoT network security through deep learning-powered Intrusion Detection System
inferredcites →https://doi.org/10.1016/j.jclinepi.2015.02.010openalex-snapshot
The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases
inferredcites →https://doi.org/10.1016/j.neucom.2015.08.104openalex-snapshot
Auto-encoder based dimensionality reduction
inferredcites →https://doi.org/10.1016/j.neucom.2019.11.016openalex-snapshot
A novel statistical analysis and autoencoder driven intelligent intrusion detection approach
inferredcites →https://doi.org/10.1016/j.patrec.2005.10.010openalex-snapshot
An introduction to ROC analysis
inferredcites →https://doi.org/10.1017/s026988891300043xopenalex-snapshot
One-class classification: taxonomy of study and review of techniques
inferredcites →https://doi.org/10.1109/access.2019.2933602openalex-snapshot
Discriminative Autoencoding Framework for Simple and Efficient Anomaly Detection
inferredcites →https://doi.org/10.1109/access.2021.3073408openalex-snapshot
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review
inferredcites →https://doi.org/10.1109/cscloud.2017.39openalex-snapshot
Network Anomaly Detection with Stochastically Improved Autoencoder Based Models
inferredcites →https://doi.org/10.1109/icassp.2016.7472159openalex-snapshot
Batch normalized recurrent neural networks
inferredcites →https://doi.org/10.1109/icpr.2018.8546162openalex-snapshot
An Effective Deep Learning Based Scheme for Network Intrusion Detection
inferredcites →https://doi.org/10.1145/2689746.2689747openalex-snapshot
Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction
inferredcites →https://doi.org/10.1155/2017/4184196openalex-snapshot
Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders
inferredcites →https://doi.org/10.11591/ijece.v12i1.pp660-668openalex-snapshot
Comparative study between metaheuristic algorithms for internet of things wireless nodes localization
inferredcites →https://doi.org/10.1186/s13677-018-0123-6openalex-snapshot
Intrusion detection systems for IoT-based smart environments: a survey
inferredcites →https://doi.org/10.1186/s42400-019-0038-7openalex-snapshot
Survey of intrusion detection systems: techniques, datasets and challenges
inferredcites →https://doi.org/10.1504/ijbic.2023.134981openalex-snapshot
Botnet attack detection in IoT using hybrid optimisation enabled deep stacked autoencoder network
inferredcites →https://doi.org/10.33564/ijeast.2020.v04i12.054openalex-snapshot
ACTIVATION FUNCTIONS IN NEURAL NETWORKS
inferredcites →https://doi.org/10.3390/app11188383openalex-snapshot
Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review
inferredcites →https://doi.org/10.3390/computers12020034openalex-snapshot
A Novel Deep Learning-Based Intrusion Detection System for IoT Networks
inferredcites →https://doi.org/10.3390/electronics11142146openalex-snapshot
Self-Supervised Learning for Time-Series Anomaly Detection in Industrial Internet of Things
inferredcites →https://doi.org/10.3390/fi15080278openalex-snapshot
An Improved Deep Learning Model for DDoS Detection Based on Hybrid Stacked Autoencoder and Checkpoint Network
inferredcites →https://doi.org/10.3390/info14020103openalex-snapshot
A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems
inferredcites →https://doi.org/10.3390/info15100583openalex-snapshot
Smart Collaborative Intrusion Detection System for Securing Vehicular Networks Using Ensemble Machine Learning Model
inferredcites →https://doi.org/10.3390/s22010032openalex-snapshot
Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems
inferredcites →https://doi.org/10.3390/s23084141openalex-snapshot
A Lightweight Intelligent Network Intrusion Detection System Using One-Class Autoencoder and Ensemble Learning for IoT
inferredcites →https://doi.org/10.3390/s23125644openalex-snapshot
Conditional Tabular Generative Adversarial Based Intrusion Detection System for Detecting Ddos and Dos Attacks on the Internet of Things Networks
inferredcites →https://doi.org/10.5120/3399-4730openalex-snapshot
A Review of Anomaly based Intrusion Detection Systems
inferredcites →https://doi.org/10.7717/peerj-cs.659openalex-snapshot
Emerging IoT domains, current standings and open research challenges: a review
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-032-18093-3_8openalex-snapshot
Secure and Efficient Intrusion Detection for IoT Networks Using Hybrid Deep Learning Models with Constrained Twin Variational Auto-Encoder and Transformer-BiGRU Architecture
inferred← citeshttps://doi.org/10.1007/978-3-032-18316-3_4openalex-snapshot
Detecting Outliers from Wireless Sensor Networks Using Reinforcement Learning
inferred← citeshttps://doi.org/10.1007/s42044-025-00334-9openalex-snapshot
Dual-level contextual graph-informed neural network with starling murmuration optimization for securing cloud-based botnet attack detection in wireless sensor networks
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111364openalex-snapshot
An explainable artificial intelligence driven fall system for sensor data analysis enhanced by butterworth filtering
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.133134openalex-snapshot
A lightweight and robust anomaly detection framework for High-Dimensional dynamic industrial Systems: RIF
inferred← citeshttps://doi.org/10.1038/s41598-025-20445-4openalex-snapshot
Robust IoT security using isolation forest and one class SVM algorithms
inferred← citeshttps://doi.org/10.1038/s41598-026-68411-yopenalex-snapshot
A reproducible neuro-fuzzy framework for interpretable anomaly detection in wireless sensor network data streams
inferred← citeshttps://doi.org/10.1093/jcde/qwaf085openalex-snapshot
A robust anomaly detector for imbalanced industrial internet of things data
inferred← citeshttps://doi.org/10.1109/compas67506.2025.11381766openalex-snapshot
An Optimized Decision Tree-Based Framework for Explainable IoT Anomaly Detection
inferred← citeshttps://doi.org/10.1109/ibaaids66771.2026.11567421openalex-snapshot
AI-Driven Anomaly Detection in IoT Networks: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1109/icaic67076.2026.11395763openalex-snapshot
Non-Intrusive Machine Learning-Based Anomaly Detection for Heterogeneous Embedded Platforms
inferred← citeshttps://doi.org/10.1109/icc59461.2026.11588158openalex-snapshot
An Intelligent Framework for Intrusion Detection in Resource-Constrained Wireless Sensor Networks
inferred← citeshttps://doi.org/10.1109/iccit68739.2025.11491365openalex-snapshot
Cross-Dataset Evaluation of Lightweight One-Class Anomaly Detectors for IoT Networks
inferred← citeshttps://doi.org/10.1109/icisessc68634.2026.11542803openalex-snapshot
Smart Thermoelectric IoT-Based Monitoring Systems (Detecting Anomalies)
inferred← citeshttps://doi.org/10.1109/icndsa68777.2026.11652227openalex-snapshot
Multi-Objective WCO-SLSTM Framework for Energy-Efficient Anomaly Detection in IoT Networks
inferred← citeshttps://doi.org/10.1109/iraset68627.2026.11538433openalex-snapshot
Autoencoders versus ML Classifiers for IoT Intrusion Detection: A critical Analysis
inferred← citeshttps://doi.org/10.1109/jiot.2026.3671102openalex-snapshot
Anomaly Detection in IoT Environment Using Machine Learning: A Survey
inferred← citeshttps://doi.org/10.1109/rice67503.2025.11439969openalex-snapshot
RBF-Enhanced QAE-GMM ADS: Scalable Intrusion Detection for Resource-Constrained IoT Devices
inferred← citeshttps://doi.org/10.1109/rivf68649.2025.11365160openalex-snapshot
Joint Representation Learning and Clustering for IoT Network Anomaly Detection
inferred← citeshttps://doi.org/10.1109/snpd65828.2025.11254972openalex-snapshot
Advancing Zero Trust Security in Industrial IoT (IIoT): A Machine Learning Perspective
inferred← citeshttps://doi.org/10.3390/electronics14142916openalex-snapshot
A Comparative Study of Dimensionality Reduction Methods for Accurate and Efficient Inverter Fault Detection in Grid-Connected Solar Photovoltaic Systems
inferred← citeshttps://doi.org/10.3390/fi18060310openalex-snapshot
TLS-Aware Anomaly Detection for Encrypted IoT Traffic Using a β-Variational Autoencoder with ANOVA–Mutual Information Feature Selection
inferred← citeshttps://doi.org/10.3390/fi18090447openalex-snapshot
Calibration Granularity, Not Contamination: Diagnosing a TCN Anomaly Detector’s False Positive Advantage in Cross-Dataset IoT Traffic
inferred← citeshttps://doi.org/10.3390/limnolrev26030051openalex-snapshot
Artificial Intelligence and Edge Computing for Sustainable Smart Water-Safety Monitoring in Low-Resource Communities: A Critical Review
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.