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Securing Internet of Things (IoT) with machine learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1006/dspr.1999.0361openalex-snapshot
Speaker Verification Using Adapted Gaussian Mixture Models
inferredcites →https://doi.org/10.1007/1-84628-253-5openalex-snapshot
Machine Learning and Data Mining for Computer Security: Methods and Applications
inferredcites →https://doi.org/10.1007/s00357-005-0012-9openalex-snapshot
Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method
inferredcites →https://doi.org/10.1016/c2009-0-19715-5openalex-snapshot
Data Mining: Practical Machine Learning Tools and Techniques
inferredcites →https://doi.org/10.1016/j.eswa.2009.05.029openalex-snapshot
Intrusion detection by machine learning: A review
inferredcites →https://doi.org/10.1016/j.future.2017.09.083openalex-snapshot
Toward efficient smartification of the Internet of Things (IoT) services
inferredcites →https://doi.org/10.1016/j.future.2018.06.027openalex-snapshot
Taxonomy and analysis of security protocols for Internet of Things
inferredcites →https://doi.org/10.1016/j.neunet.2005.06.025openalex-snapshot
Handling missing values in support vector machine classifiers
inferredcites →https://doi.org/10.1016/j.proeng.2011.08.036openalex-snapshot
Anomaly Detection Based on Enhanced DBScan Algorithm
inferredcites →https://doi.org/10.1016/s0167-4048(02)00514-xopenalex-snapshot
Use of K-Nearest Neighbor classifier for intrusion detection
inferredcites →https://doi.org/10.1023/a:1007607513941openalex-snapshot
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
inferredcites →https://doi.org/10.1080/00031305.2015.1086685openalex-snapshot
k-POD: A Method fork-Means Clustering of Missing Data
inferredcites →https://doi.org/10.1109/access.2016.2565198openalex-snapshot
A Mobile Offloading Game Against Smart Attacks
inferredcites →https://doi.org/10.1109/access.2019.2912200openalex-snapshot
Review of Deep Learning Algorithms and Architectures
inferredcites →https://doi.org/10.1109/cns.2013.6682689openalex-snapshot
Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning
inferredcites →https://doi.org/10.1109/comst.2014.2320099openalex-snapshot
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
inferredcites →https://doi.org/10.1109/comswa.2008.4554519openalex-snapshot
MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks
inferredcites →https://doi.org/10.1109/hase.2016.48openalex-snapshot
Game Theory with Learning for Cyber Security Monitoring
inferredcites →https://doi.org/10.1109/i-smac.2017.8058363openalex-snapshot
Security attacks in IoT: A survey
inferredcites →https://doi.org/10.1109/icdcs.2006.49openalex-snapshot
In-Network Outlier Detection in Wireless Sensor Networks
inferredcites →https://doi.org/10.1109/iced.2016.7804660openalex-snapshot
Internet of Things (IoT): Taxonomy of security attacks
inferredcites →https://doi.org/10.1109/icmla.2013.105openalex-snapshot
An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications
inferredcites →https://doi.org/10.1109/ifita.2009.34openalex-snapshot
The Application on Intrusion Detection Based on K-means Cluster Algorithm
inferredcites →https://doi.org/10.1109/iisa.2017.8316459openalex-snapshot
A brief survey of machine learning methods and their sensor and IoT applications
inferredcites →https://doi.org/10.1109/ijcnn.2002.1007774openalex-snapshot
Intrusion detection using neural networks and support vector machines
inferredcites →https://doi.org/10.1109/iscc.2015.7405513openalex-snapshot
Internet of Things: Security vulnerabilities and challenges
inferredcites →https://doi.org/10.1109/jiot.2017.2704093openalex-snapshot
Heimdall: Mitigating the Internet of Insecure Things
inferredcites →https://doi.org/10.1109/mc.2017.201openalex-snapshot
DDoS in the IoT: Mirai and Other Botnets
inferredcites →https://doi.org/10.1109/mc.2017.62openalex-snapshot
Botnets and Internet of Things Security
inferredcites →https://doi.org/10.1109/milcom.2017.8170867openalex-snapshot
The Mirai botnet and the IoT Zombie Armies
inferredcites →https://doi.org/10.1109/mnet.2018.1700202openalex-snapshot
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
inferredcites →https://doi.org/10.1109/msp.2018.2825478openalex-snapshot
IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?
inferredcites →https://doi.org/10.1109/pccc.2014.7017082openalex-snapshot
ZigBee vs WiFi: Understanding issues and measuring performances of their coexistence
inferredcites →https://doi.org/10.1109/pst.2016.7906930openalex-snapshot
Using machine learning to secure IoT systems
inferredcites →https://doi.org/10.1109/sice.2006.315751openalex-snapshot
ZigBee Wireless Sensor Network in Industrial Applications
inferredcites →https://doi.org/10.1109/sp.2010.25openalex-snapshot
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
inferredcites →https://doi.org/10.1109/tifs.2013.2286269openalex-snapshot
Proximity-Based Security Techniques for Mobile Users in Wireless Networks
inferredcites →https://doi.org/10.1109/tit.1982.1056489openalex-snapshot
Least squares quantization in PCM
inferredcites →https://doi.org/10.1109/tsp.2012.2223695openalex-snapshot
Gaussian Process Regression for Sensor Networks Under Localization Uncertainty
inferredcites →https://doi.org/10.1109/waina.2012.135openalex-snapshot
Challenges of Machine Learning Based Monitoring for Industrial Control System Networks
inferredcites →https://doi.org/10.1109/wicom.2007.638openalex-snapshot
A Design of Greenhouse Monitoring & Control System Based on ZigBee Wireless Sensor Network
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1809.02206openalex-snapshot
Challenges of Context and Time in Reinforcement Learning: Introducing Space Fortress as a Benchmark
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Machine Learning-Based Cyberattacks Detection Enhancement in Iot Environments with Imbalanced Data Handling
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A systematic literature review of machine learning applications in IoT
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Is This IoT Device Likely to Be Secure? Risk Score Prediction for IoT Devices Using Gradient Boosting Machines
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Exploring IoT Communication Technologies and Data-Driven Solutions
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Lightweight Intrusion Detection for IoT Systems Using Artificial Neural Networks
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Classification of Attacks on MQTT-Based IoT System Using Machine Learning Techniques
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Optimum Outlier Detection in Internet of Things Industries Using Autoencoder
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A Review of Machine Learning-Based Security Algorithms for the Internet of Things
inferred← citeshttps://doi.org/10.1007/s10586-024-04509-0openalex-snapshot
Securing internet of things using machine and deep learning methods: a survey
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A systematic review of innovations for real-time image security in IoT applications using machine learning and blockchain
inferred← citeshttps://doi.org/10.1007/s40031-025-01221-4openalex-snapshot
Botnet Detection Through Flow-Based Deep Feature Extraction and Ensemble Classification
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Internet of things: Conceptual network structure, main challenges and future directions
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Data-oriented ensemble predictor based on time series classifiers for fraud detection
inferred← citeshttps://doi.org/10.1016/j.iotcps.2023.12.003openalex-snapshot
Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models
inferred← citeshttps://doi.org/10.1016/j.iswa.2023.200189openalex-snapshot
Engineering the application of machine learning in an IDS based on IoT traffic flow
inferred← citeshttps://doi.org/10.1016/j.jnca.2020.102630openalex-snapshot
Machine learning based solutions for security of Internet of Things (IoT): A survey
inferred← citeshttps://doi.org/10.1016/j.matpr.2020.10.187openalex-snapshot
WITHDRAWN: Improved security using machine learning for IoT intrusion detection system
inferred← citeshttps://doi.org/10.1016/j.swevo.2024.101653openalex-snapshot
IOT device type identification using magnetized Hopfield neural network with tuna swarm optimization algorithm
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Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques
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SDR implementation of a DSSS detector based Light CNN for cognitive radio
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Machine Learning Challenges for IoT Device Fingerprints Identification
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A novel approach to enhance fault tolerance in vote-based blockchain consensus algorithm using machine learning mechanism
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Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity
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Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey
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A Review on the Security of IoT Networks: From Network Layer’s Perspective
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Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directions
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Deep Neural Networks for Enhanced Security: Detecting Metamorphic Malware in IoT Devices
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FLCMed-TAD: An Anomaly-Aware Federated Learning Approach for EV Load Forecasting
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A Systematic Literature Review on Artificial Intelligence Applications for Internet of Things Security
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Machine Learning application for Smart Home Threat Detection using the HIKARI-2021 Data-set
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How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey
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Exploring the Potential of IoT and Advanced Technologies for Smart and Sustainable Living
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Comparative Evaluation on Various Machine Learning Strategies Based on Identification of DDoS Attacks in IoT Environment
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Ultra-Lightweight and Secure Intrusion Detection System for Massive-IoT Networks
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Anomaly-Based IDS In IOT Healthcare Application
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Machine Learning Performance on IoT Device
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Implementation of Cyber Threat Intelligence Platform on Internet of Things (IoT) using TinyML Approach for Deceiving Cyber Invasion
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DDoS Attack Detection in IoT Using Machine Learning Based Intrusion Detection System (IDS)
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Methods for Securing IoT Devices Using Machine Learning and Deep Learning: An Evaluation
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Real or Fake Identity Deception of Social Media Accounts using Recurrent Neural Network
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Identification of a Person From Live Video Streaming Using Machine Learning in the Internet of Things (IoT)
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Intrusion Detection in IOT Networks using Machine Learning Techniques
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