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An Anomaly Mitigation Framework for IoT Using Fog Computing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/ett.3183openalex-snapshot
Leveraging fog computing and software defined systems for selective forwarding attacks detection in mobile wireless sensor networks
inferredcites →https://doi.org/10.1002/ett.3188openalex-snapshot
Anomaly behavior analysis for IoT sensors
inferredcites →https://doi.org/10.1002/ett.3803openalex-snapshot
DL‐IDS: a deep learning–based intrusion detection framework for securing IoT
inferredcites →https://doi.org/10.1016/j.adhoc.2013.04.014openalex-snapshot
SVELTE: Real-time intrusion detection in the Internet of Things
inferredcites →https://doi.org/10.1016/j.asoc.2018.05.049openalex-snapshot
Semi-supervised learning based distributed attack detection framework for IoT
inferredcites →https://doi.org/10.1016/j.future.2013.01.010openalex-snapshot
Internet of Things (IoT): A vision, architectural elements, and future directions
inferredcites →https://doi.org/10.1016/j.future.2017.08.043openalex-snapshot
Distributed attack detection scheme using deep learning approach for Internet of Things
inferredcites →https://doi.org/10.1016/j.future.2017.11.022openalex-snapshot
IoT security: Review, blockchain solutions, and open challenges
inferredcites →https://doi.org/10.1016/j.future.2019.04.038openalex-snapshot
Perception layer security in Internet of Things
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.jisa.2017.11.002openalex-snapshot
Internet of Things: A survey on the security of IoT frameworks
inferredcites →https://doi.org/10.1016/j.jisa.2018.05.002openalex-snapshot
Identification of malicious activities in industrial internet of things based on deep learning models
inferredcites →https://doi.org/10.1016/j.jnca.2016.10.007openalex-snapshot
Survey of approaches and features for the identification of HTTP-based botnet traffic
inferredcites →https://doi.org/10.1016/j.jnca.2017.02.009openalex-snapshot
A survey of intrusion detection in Internet of Things
inferredcites →https://doi.org/10.1016/j.jnca.2018.12.006openalex-snapshot
A holistic review of Network Anomaly Detection Systems: A comprehensive survey
inferredcites →https://doi.org/10.1016/j.promfg.2019.02.292openalex-snapshot
A Real-Time Intrusion Detection System for Wormhole Attack in the RPL based Internet of Things
inferredcites →https://doi.org/10.1017/s0960129512000783openalex-snapshot
Shannon entropy: a rigorous notion at the crossroads between probability, information theory, dynamical systems and statistical physics
inferredcites →https://doi.org/10.1037/e471672008-001openalex-snapshot
Discriminatory analysis: Nonparametric discrimination: Consistency properties
inferredcites →https://doi.org/10.1109/access.2018.2798626openalex-snapshot
Compression Header Analyzer Intrusion Detection System (CHA - IDS) for 6LoWPAN Communication Protocol
inferredcites →https://doi.org/10.1109/access.2019.2907965openalex-snapshot
Toward a Lightweight Intrusion Detection System for the Internet of Things
inferredcites →https://doi.org/10.1109/access.2020.2976624openalex-snapshot
Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks
inferredcites →https://doi.org/10.1109/ccnc.2019.8651782openalex-snapshot
A Two-Level Hybrid Model for Anomalous Activity Detection in IoT Networks
inferredcites →https://doi.org/10.1109/ccwc.2019.8666450openalex-snapshot
AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning
inferredcites →https://doi.org/10.1109/comst.2015.2444095openalex-snapshot
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
inferredcites →https://doi.org/10.1109/iot-siu.2019.8777504openalex-snapshot
ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things
inferredcites →https://doi.org/10.1109/jiot.2017.2709942openalex-snapshot
Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering
inferredcites →https://doi.org/10.1109/jiot.2019.2926365openalex-snapshot
A Supervised Intrusion Detection System for Smart Home IoT Devices
inferredcites →https://doi.org/10.1109/surv.2013.052213.00046openalex-snapshot
Network Anomaly Detection: Methods, Systems and Tools
inferredcites →https://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0080openalex-snapshot
RPiDS: Raspberry Pi IDS — A Fruitful Intrusion Detection System for IoT
inferredcites →https://doi.org/10.1109/wimob.2013.6673419openalex-snapshot
Denial-of-Service detection in 6LoWPAN based Internet of Things
inferredcites →https://doi.org/10.1142/s0218001405003983openalex-snapshot
EXPLORING CONDITIONS FOR THE OPTIMALITY OF NAÏVE BAYES
inferredcites →https://doi.org/10.1145/2342509.2342513openalex-snapshot
Fog computing and its role in the internet of things
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/10.2307/1403797openalex-snapshot
Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties
inferredcites →https://doi.org/10.23919/icact.2018.8323766openalex-snapshot
A PCA-based method for IoT network traffic anomaly detection
inferredcites →https://doi.org/10.3390/info9070149openalex-snapshot
Effective Intrusion Detection System Using XGBoost
inferredcites →https://doi.org/10.3390/s90805989openalex-snapshot
Intrusion-Aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks
inferredcites →https://doi.org/10.4018/ijfc.2020010105openalex-snapshot
Fog Computing Architecture, Applications and Security Issues
inferredcites →https://doi.org/10.4135/9781412983907.n1425openalex-snapshot
Pearson's Correlation Coefficient
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.70605openalex-snapshot
Anomaly Detection in IoT Environments Using Machine Learning: A Bibliometric Review, Challenges, and Future Research Directions
inferred← citeshttps://doi.org/10.1002/ett.70064openalex-snapshot
An Extensive Review of Machine Learning and Deep Learning Techniques on Network Intrusion Detection for IoT
inferred← citeshttps://doi.org/10.1002/ett.70268openalex-snapshot
ResA ‐ D 2 PySepCo : Cyber‐Attack Detection in Fog Based IoT Network Using Pyramidal Dilated Separable Residual Convolutional With Effective Optimization Algorithm
inferred← citeshttps://doi.org/10.1007/978-3-031-08270-2_7openalex-snapshot
Utilising K-Means Clustering and Naive Bayes for IoT Anomaly Detection: A Hybrid Approach
inferred← citeshttps://doi.org/10.1007/978-3-031-98138-8_15openalex-snapshot
A Systematic Review on Anomaly Detection Techniques for Fog Computing Devices
inferred← citeshttps://doi.org/10.1007/978-981-16-8059-5_30openalex-snapshot
Intrusion Detection Systems in Fog Computing – A Review
inferred← citeshttps://doi.org/10.1007/978-981-19-1018-0_3openalex-snapshot
MLP Deep Learning-based DDoS Attack Detection Framework for Fog Computing
inferred← citeshttps://doi.org/10.1007/978-981-19-5224-1_24openalex-snapshot
Attack Detection in Internet of Things: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1007/s10586-025-05100-xopenalex-snapshot
An edge-enhanced graphSAGE-based intrusion detection model for the internet of things
inferred← citeshttps://doi.org/10.1007/s11042-024-18535-yopenalex-snapshot
Security establishment using deep convolutional network model in cyber-physical systems
inferred← citeshttps://doi.org/10.1007/s44227-025-00079-8openalex-snapshot
Comparative Analysis of Machine and Deep Learning Models for Intrusion Detection in Fog-Enabled IoT Networks
inferred← citeshttps://doi.org/10.1016/b978-0-443-33441-2.00003-3openalex-snapshot
An intelligent framework to secure fog computer and IoT service applications using artificial intelligence/machine learning
inferred← citeshttps://doi.org/10.1016/j.comnet.2022.109154openalex-snapshot
Intrusion detection and prevention in fog based IoT environments: A systematic literature review
inferred← citeshttps://doi.org/10.1016/j.comnet.2025.111157openalex-snapshot
UGL: A comprehensive hybrid model integrating GCN and LSTM for enhanced intrusion detection in UAV controller area networks
inferred← citeshttps://doi.org/10.1016/j.comnet.2026.112760openalex-snapshot
DFA-SAGE: A Dual-Fusion Aggregation Graph sampling and embedding model for enhanced intrusion detection
inferred← citeshttps://doi.org/10.1016/j.cose.2024.104132openalex-snapshot
Edge-featured multi-hop attention graph neural network for intrusion detection system
inferred← citeshttps://doi.org/10.1016/j.cose.2024.104217openalex-snapshot
A reliability anomaly detection method based on enhanced GRU-Autoencoder for Vehicular Fog Computing services
inferred← citeshttps://doi.org/10.1016/j.mlwa.2021.100156openalex-snapshot
Genetic Algorithm based feature selection and Naïve Bayes for anomaly detection in fog computing environment
inferred← citeshttps://doi.org/10.1049/wss2.12100openalex-snapshot
Intrusion detection in cluster‐based wireless sensor networks: Current issues, opportunities and future research directions
inferred← citeshttps://doi.org/10.1051/matecconf/202439201103openalex-snapshot
Securing IoT networks: A fog-based framework for malicious device detection
inferred← citeshttps://doi.org/10.1063/5.0317848openalex-snapshot
Simulation and modelling for anomaly detection in body sensor networks using machine learning classification techniques
inferred← citeshttps://doi.org/10.1109/access.2022.3206367openalex-snapshot
Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks
inferred← citeshttps://doi.org/10.1109/access.2023.3246660openalex-snapshot
Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network
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Graph Learning Framework for Data Link Anomaly Detection
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Artificial Intelligence Techniques for Securing Fog Computing Environments: Trends, Challenges, and Future Directions
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Adaptive Security Framework for the Internet of Things: Improving Threat Detection and Energy Optimization in Distributed Environments
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Anomaly Detection for IoT Networks: Empirical Study
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Fog Computing: A Systematic Review from the Perspectives of Architecture, Business Models, and Research Developments
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Graph-based Intrusion Detection System Using General Behavior Learning
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Multiclass Intrusion Detection System for IoT Networks Using Gradient Boosted Trees
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E-GraphSAGE with Explainability: Edge-Centric Graph Neural Networks for IoT Intrusion Detection across Heterogeneous Datasets
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A Hybrid Approach to Network Intrusion Detection Based On Graph Neural Networks and Transformer Architectures
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A Scrutiny of Privacy and Security Issues in Fog Computing Environment
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IoT-based DDoS on Cyber Physical Systems: Research Challenges, Datasets and Future Prospects
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Six-GraphSecurity: Industrial Internet Intrusion Detection Based On Graph Neural Network
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Hardware/Software Codesign of Real-Time Intrusion Detection System for Internet of Things Devices
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Enhancing Network Anomaly Detection Using Graph Neural Networks
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A Comprehensive Analysis of Fog Layer and Man in the Middle Attacks in IoT Networks
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E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
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On the Fence: Anomaly Detection in IoT Networks
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Systematic Review for the Construction of an Architecture With Emerging IoT Technologies, Artificial Intelligence Techniques, Monitoring and Storage of Malicious Traffic
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A Review and Analysis of the Bot-IoT Dataset
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Federated Learning-Based Misbehavior Detection for the 5G-Enabled Internet of Vehicles
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DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications
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A Deep Intelligent Attack Detection Framework for Fog-Based IoT Systems
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Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks
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BS-GAT: a network intrusion detection system based on graph neural network for edge computing
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LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes
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IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
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Anomaly Detection of Zero-Day Attacks Based on CNN and Regularization Techniques
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Challenges of Network Forensic Investigation in Fog and Edge Computing
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Fog-Deployable XGBoost Framework for DDoS Detection in IoT Networks
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