Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

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Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches

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A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets

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Machine Learning DDoS Detection for Consumer Internet of Things Devices

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Security and privacy in smart farming: challenges and opportunities

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Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100227 · Cited by 488

Machine learning based solutions for security of Internet of Things (IoT): A survey

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Deep recurrent neural network for IoT intrusion detection system

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A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting

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Threat analysis of IoT networks using artificial neural network intrusion detection system

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Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city

Shafiq, M., Tian, Z., Sun, Y., Du, X., & Guizani, M. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.02.017 · Cited by 384

A deep learning method with wrapper based feature extraction for wireless intrusion detection system

Kasongo, S. M., & Sun, Y. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101752 · Cited by 373

Artificial intelligence in Internet of things

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Deep Learning Models for Cyber Security in IoT Networks

Roopak, M., Yun Tian, G., & Chambers, J. (2019) · 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666588 · Cited by 361

Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network

Zhang, Y., Li, P., & Wang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2903723 · Cited by 359

Semi-supervised learning based distributed attack detection framework for IoT

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State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review

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Robust detection for network intrusion of industrial IoT based on multi-CNN fusion

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Predicting COVID-19 in China Using Hybrid AI Model

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AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning

Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., & Ming, H. (2019) · 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666450 · Cited by 307

Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Thamilarasu, G., & Chawla, S. (2019) · Sensors · doi:10.3390/s19091977 · Cited by 303

Hybrid Intrusion Detection System for Internet of Things (IoT)

Smys, D. S., Basar, D. A., & Wang, D. H. (2020) · Journal of ISMAC · doi:10.36548/jismac.2020.4.002 · Cited by 299

Detecting crypto-ransomware in IoT networks based on energy consumption footprint

Azmoodeh, A., Dehghantanha, A., Conti, M., & Choo, K.-K. R. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-017-0558-5 · Cited by 293

Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

Farivar, F., Haghighi, M. S., Jolfaei, A., & Alazab, M. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2956474 · Cited by 293

A Machine Learning Security Framework for Iot Systems

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Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach

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An intrusion detection system using network traffic profiling and online sequential extreme learning machine

Singh, R., Kumar, H., & Singla, R. K. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.015 · Cited by 262

Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets

Sáez, J. A., Krawczyk, B., & Woźniak, M. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.012 · Cited by 242

Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Wu, M., Song, Z., & Moon, Y. B. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-017-1315-5 · Cited by 238

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

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Scalable machine learning-based intrusion detection system for IoT-enabled smart cities

Rahman, M. A., Asyhari, A. T., Leong, L. S., Satrya, G. B., Hai Tao, M., & Zolkipli, M. F. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102324 · Cited by 219

Fuzzy pattern tree for edge malware detection and categorization in IoT

Dovom, E. M., Azmoodeh, A., Dehghantanha, A., Newton, D. E., Parizi, R. M., & Karimipour, H. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.01.017 · Cited by 212

IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning

Li, D., Deng, L., Lee, M., & Wang, H. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.006 · Cited by 212

An Ensemble of Deep Recurrent Neural Networks for Detecting IoT Cyber Attacks Using Network Traffic

Saharkhizan, M., Azmoodeh, A., Dehghantanha, A., Choo, K.-K. R., & Parizi, R. M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2996425 · Cited by 208

The Challenges of IoT Addressing Security, Ethics, Privacy, and Laws

Karale, A. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100420 · Cited by 208

Detection of power grid disturbances and cyber-attacks based on machine learning

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Internet of Things Meet Internet of Threats: New Concern Cyber Security Issues of Critical Cyber Infrastructure

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Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures

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Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

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Anomaly Detection Using Deep Neural Network for IoT Architecture

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An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

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A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures

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Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment

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Privacy preservation for machine learning training and classification based on homomorphic encryption schemes

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A systematic review on Deep Learning approaches for IoT security

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Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks

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Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments

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Proposing a Secure RPL based Internet of Things Routing Protocol: A Review

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Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

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CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks

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Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph

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Toward Detection and Attribution of Cyber-Attacks in IoT-Enabled Cyber–Physical Systems

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Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction

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Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

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Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

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Generating Fake Cyber Threat Intelligence Using Transformer-Based Models

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An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

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DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System

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Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status

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Emerging IoT Applications in Sustainable Smart Cities for COVID-19: Network Security and Data Preservation Challenges with Future Directions

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Denial-of-Service, Probing & Remote to User (R2L) Attack Detection using Genetic Algorithm

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Progressive researches on IoT security: An exhaustive analysis from the perspective of protocols, vulnerabilities, and preemptive architectonics

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Artificial intelligence and the future of cybersecurity

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A wearable-based posture recognition system with AI-assisted approach for healthcare IoT

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Editorial: Blockchain in Industrial IoT Applications: Security and Privacy Advances, Challenges, and Opportunities

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Machine Learning Cyberattack and Defense Strategies

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Explainable Security in SDN-Based IoT Networks

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Federated learning for intrusion detection in IoT security: a hybrid ensemble approach

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Explainable artificial intelligence to evaluate industrial internal security using EEG signals in IoT framework

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An integrity verification scheme of cloud storage for internet-of-things mobile terminal devices

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MMDA: Multidimensional and multidirectional data aggregation for edge computing-enhanced IoT

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Building A Fast Intrusion Detection System For High-Speed-Networks: Probe and DoS Attacks Detection

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A pragmatic elliptic curve cryptography-based extension for energy-efficient device-to-device communications in smart cities

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Collaborative algorithms that combine AI with IoT towards monitoring and control system

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Anomaly Based Unknown Intrusion Detection in Endpoint Environments

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Multivariate Gaussian-Based False Data Detection Against Cyber-Attacks

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Cybersecurity Model Based on Hardening for Secure Internet of Things Implementation

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Intelligent Asset Allocation using Predictions of Deep Frequency Decomposition

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A Distributed Service Framework for the Internet of Things

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Discussion on IoT Security Recommendations against the State-of-the-Art Solutions

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Smart Cities in the Era of Artificial Intelligence and Internet of Things: Promises and Challenges

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Secure monitoring in IoT-based services via fog orchestration

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Nonequilibrium thermodynamics of self-supervised learning

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Explainable intrusion detection for cyber defences in the internet of things: opportunities and solutions

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Application of Large Language Models to DDoS Attack Detection

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Automated cyberattack detection using optimal ensemble deep learning model

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