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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Open accessAbstract
The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.
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“Al-Garadi et al. [32] and Pakalapati et al. [33] provide strategic assessments of using AI/ML for threat detection and automated decision-making within DevSecOps pipelines, signalling the start of a broader movement toward intelligent, data-driven security automation.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [32]
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“Among the potential surveyed applications, ML has been used mainly for intrusion detection, while DL for malware and anomalies detection.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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In the citing paper
“The review is primarily focused on IoT environments and does not directly address the integration of ML/DL into DevSecOps pipelines, which may limit its relevance for general DevSecOps practices.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [32]
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“As main conclusions, the authors claim the need for diverse datasets within IoT security.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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“Several security aspects shall be considered in every IoT system (Al-Garadi et al. 2020):”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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“For all of these explanations, IoT systems need to go from simply enabling secure communication between devices to intelligence provided by DL approaches to create robust, all-encompassing security solutions, as noted by the al2020survey.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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Cites
1 retracted
Deng, L., Li, D., Yao, X., & Wang, H. (2018) · Cluster Computing · doi:10.1007/s10586-018-1847-2 · Cited by 124
RetractedBreiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Flagged first, then by citations
Research works printed but not linked: 17
- lakhotia, 2004, Are metamorphic viruses really invincible, Virus Bull, 12, 57
- zheng, 2018, IoT Malware Evolves to Harvest Bots by Exploiting a Zero-Day Home Router Vulnerability
- baba, 2015, Current issues in ensemble methods and its applications, J Theor Appl Inf Technol, 81, 266
- münz, 2007, Traffic anomaly detection using k-means clustering, Proc GI/ITG Workshop MMBnet, 1
- adetunmbi, 2008, Network intrusion detection based on rough set and k-nearest neighbor, Int J Comput ICT Res, 2, 60
- 2017, Internet of medical things forecast to 2021
- 10.1007/BF03219883 · DOI
- goodfellow, 2016, Deep Learning
- zhu, 2005, Semi-supervised learning literature survey
- clevert, 2015, Fast and accurate deep network learning by exponential linear units (ELUs)
- mohammadi, 2017, Deep Learning for IoT Big Data and Streaming Analytics A Survey
- goodfellow, 2014, Generative adversarial nets, Proc Adv Neural Inf Process Syst, 2672
- tong, 2001, Support vector machine active learning with applications to text classification, J Mach Learn Res, 2, 45
- nguyen, 2019, Secure computation offloading in blockchain based IoT networks with deep reinforcement learning
- dulac-arnold, 2019, Challenges of Real-World Reinforcement Learning
- nguyen, 2019, Deep reinforcement learning for cyber security
- 2018, Mobile Apps Leveraging the Internet of Things (IoT)
Cited by
Sorted by citations
Items 801–900 of 1,001
Alenezi, M. N. (2025) · Future Internet · doi:10.3390/fi17060266 · Cited by 7
A Comparative Study Between Solutions Proposed to Secure IoT Networks
Bachar, M., Khiat, A., & Bahnasse, A. (2025) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc66714.2025.11135134 · Cited by 4
Qudah, D. A., Al-Zoubi, A. M., Al-Shboul, B. A., Abu-Salih, B., Qaddoura, R., & Hijjawi, M. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00237-y · Cited by 4
Eryılmaz, E. E., Akleylek, S., Ertek, Y., & Kılıç, E. (2024) · MANAS Journal of Engineering · doi:10.51354/mjen.1197753 · Cited by 4
A Review on Machine Learning Techniques for Secure IOT Networks
Kumar, A., Kushaniya, P., Kumar, V., & Kumar, A. (2023) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n60023.2023.10541477 · Cited by 4
Generative Adversarial Networks for Robust Anomaly Detection in Noisy IoT Environments
Abusitta, A. H., Halabi, T., Bataineh, A. S., & Zulkernine, M. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622882 · Cited by 4
Liu, D., & Ji, T. (2024) · Scientific Reports · doi:10.1038/s41598-024-73480-y · Cited by 4
Navigating the Cyber Battlefield: Understanding Threats and Safeguarding Digital Frontiers
Shinde, S. S., Kale, G. M., Nalbalwar, S. L., & Deosarkar, S. B. (2024) · International Conference on Electrical Electronics and Computing Technologies (ICEECT) · doi:10.1109/iceect61758.2024.10739310 · Cited by 4
Botnet Detection Through Flow-Based Deep Feature Extraction and Ensemble Classification
Geetha, K., & Brahmananda, S. H. (2025) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-025-01221-4 · Cited by 4
Enhancing Cybersecurity in IoT & IIoT: A Machine Learning Approach for Anomaly Detection
Koroma, M. J., Mansaray, A., Kamara, Y. L., Jalloh, C. G., & Bah, I. S. O. (2025) · Journal of Software Engineering and Applications · doi:10.4236/jsea.2025.186012 · Cited by 4
CRGT-SA: an interlaced and spatiotemporal deep learning model for network intrusion detection
Chen, J., Liu, W., Qiu, X., Lv, W., & Xiong, Y.-J. (2025) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2400459 · Cited by 4
Li, Z.-Z., Xiong, C., Zheng, K., & Li, Q. (2025) · IEEE Access · doi:10.1109/access.2025.3602840 · Cited by 4
A New Hybrid Adaptive Optimization Design for Wireless Sensor Networks in Smart Agriculture
Bentoumi, A., Belhani, A., & Mouetsi, S. (2025) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-025-00884-5 · Cited by 4
Khalaf, Q. M., Al-Attar, B., Pokale, N. B., Al‐Qurabat, A. K. M., Aljanabi, Y. I. H., Fadhil, R. I., Alrazaq, H. A., Divekar, N., & Sekhar, R. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292296 · Cited by 5
Enoch, S. Y., Jibril, M. L., Malgwi, Y. M., & Wajiga, G. M. (2026) · Franklin Open · doi:10.1016/j.fraope.2026.100490 · Cited by 4
Aprendizaje Profundo Frente a Reglas Estáticas
Serracín, J. R., & Gómez, J. M. (2026) · Synergía · doi:10.48204/synergia.v5n1.9883 · Cited by 4
Resource Allocation Strategy of IoT based on Network Slicing
Pang, X. L., & Zhang, P. (2020) · Computing, Communications and IoT Applications (ComComAp) · doi:10.1109/comcomap51192.2020.9398873 · Cited by 4
Analyses of Open Security Issues for Smart Home and Sensor Network Based on Internet of Things
Kim∥, J. T. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.97851 · Cited by 4
A Comparative Study of Various Intrusion Detections In Smart Cities Using Machine Learning
Basheer, L., & Ranjana, P. (2022) · International Conference on IoT and Blockchain Technology (ICIBT) · doi:10.1109/icibt52874.2022.9807724 · Cited by 4
Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization
Balaji, S., & Sankaranarayanan, S. (2022) · International Journal of Computing · doi:10.47839/ijc.21.4.2781 · Cited by 4
A Brief Overview of Deep Learning Approaches for IoT Security
Khalid, M. H., Sharif, H., Rehman, F. U., Ullah, M. N., Shaukat, S., Maqsood, H., Ali, C. N., Hussain, A., & Iftikhar, I. (2023) · International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) · doi:10.1109/icomet57998.2023.10099306 · Cited by 4
Securing Fog Computing-based Industry 4.0 Communication Using Authenticated Key Agreement Scheme
Mishra, A. K., Wazid, M., Singh, D. P., Das, A. K., & Guizani, M. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10183021 · Cited by 4
Self-citationWake-Up Security: Effective Security Improvement Mechanism for Low Power Internet of Things
Yun, S.-W., Park, N.-E., & Lee, I.-G. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.039940 · Cited by 4
Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism
Zhang, Y., Hu, J. P., Jiang, R., Lin, Z., & Chen, Z. (2023) · Entropy · doi:10.3390/e26010029 · Cited by 5
Exploring the Path Loss of a Hacking Tool for Security Matters in the Internet of Things
Mata-Hernandez, R., Cardenas‐Juarez, M., Simón, J., Stevens‐Navarro, E., & Rizzardi, A. (2023) · IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) · doi:10.1109/ropec58757.2023.10409407 · Cited by 4
Real-Time Tissue Classification Using a Novel Optical Needle Probe for Biopsy
Surażyński, Ł., Hassinen, V., Nieminen, M. T., Seppänen, T., & Myllylä, T. (2024) · Applied Spectroscopy · doi:10.1177/00037028241230568 · Cited by 4
A Review of Various Machine Learning Techniques and its Application on IoT and Cloud Computing
Talabani, H. S., & Jumaa, I. H. (2024) · Tikrit Journal of Pure Science · doi:10.25130/tjps.v29i1.1618 · Cited by 4
Discretization-Based Ensemble Model for Robust Learning in IoT
Namvar, A., Thapa, C., & Kanhere, S. S. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-63992-0_23 · Cited by 4
Generative AI for Threat Hunting and Behaviour Analysis
Sindiramutty, S. R., Jhanjhi, N. Z., Akbar, R., Hussain, M., Ray, S. K., & Amsaad, F. (2024) · Advances in digital crime, forensics, and cyber terrorism book series · doi:10.4018/979-8-3693-8944-7.ch007 · Cited by 4
A Comparative Analysis of Deep Learning Based Techniques for Cyber Security
Sangeetha, K., Kala, A., Femi, P. S., & Priya, S. B. (2024) · International Conference on Software, Systems and Information Technology (SSITCON) · doi:10.1109/ssitcon62437.2024.10796142 · Cited by 4
Kontagora, M. M., Adeshina, S. A., & Musa, H. (2025) · OALib · doi:10.4236/oalib.1112855 · Cited by 4
Reddy, M. I., Kumar, A. P. S., & Reddy, K. S. (2025) · Soft Computing · doi:10.1007/s00500-025-10521-2 · Cited by 4
Traffic Abnormality Detection ML-based Method for IoT
Ageyev, D., Радівілова, Т., Bondarenko, O., & Mohammed, O. (2021) · International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo) · doi:10.1109/ukrmico52950.2021.9716705 · Cited by 4
Homomorphic Proxy Re-Signcryption Scheme and Its Application in Edge Computing-Enhanced IoT
Yang, X., Zhou, H., Ren, N., & Tian, T. (2021) · 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT) · doi:10.1109/cecit53797.2021.00118 · Cited by 4
Predicting Cyber-Attacks on IoT Networks Using Deep-Learning and Different Variants of SMOTE
Akash, B. S., Yannam, P. K. R., Ruthvik, B. V. S., Kumar, L., Murthy, L. B., & Krishna, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-99587-4_21 · Cited by 4
Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier
Xu, Y., de Souza, R. W. R., Medeiros, E. P., Jain, N., Zhang, L., Júnior, L. A. P., & de Albuquerque, V. H. C. (2022) · Soft Computing · doi:10.1007/s00500-022-07350-y · Cited by 4
Forecasting the IoT ‐based cyber threats using the hybrid forage dependent ensemble classifier
Turukmane, A. V. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7460 · Cited by 4
Data Instrumentation From IoT Network Traffic as Support for Security Management
Vergütz, A., dos Santos, B. V., Kantarcı, B., & Nogueira, M. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3233673 · Cited by 4
Nguyen, T. N., Minh, B. V., Tran, D.-H., Le, T.-L., Le, A., Nguyen, S. Q., & Lee, B. M. (2023) · Sensors · doi:10.3390/s23177618 · Cited by 4
Synergy Between 6G and AI: Open Future Horizons and Impending Security Risks
Yaacoub, E. E. (2023) · Computer Networks and Communications · doi:10.37256/cnc.1120232969 · Cited by 4
Beating Gradient Boosting: Target-Guided Binning for Massively Scalable Classification in Real-Time
Ruta, D., Liu, M., & Cen, L. (2023) · Annals of Computer Science and Information Systems · doi:10.15439/2023f7166 · Cited by 4
IoT Fog Computing Optimization Method Based on Improved Convolutional Neural Network
Jing, B., & Xue, H. (2023) · IEEE Access · doi:10.1109/access.2023.3348133 · Cited by 4
Potentials of Machine Learning for Data analysis in IoT: A Detailed Survey
Vasoya, S., Patel, N., Ramoliya, D., & Patel, K. (2020) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss49785.2020.9316074 · Cited by 4
Internet of Things, Preliminaries and Foundations
Abdel‐Basset, M., Moustafa, N., Hawash, H., & Ding, W. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-89025-4_2 · Cited by 4
Federated Learning Cost Disparity for IoT Devices
Alvi, S. A., Hong, Y., & Durrani, S. (2022) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops53468.2022.9814533 · Cited by 4
Mobile cellular network security vulnerability detection using machine learning
Zhang, C., Wang, H., & Chen, G. (2023) · International Journal of Information and Communication Technology · doi:10.1504/ijict.2023.10054797 · Cited by 4
RADTEC: Re-authentication of IoT Devices with Machine Learning
Gupta, K., Ghose, N., & Wang, B. (2023) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc51644.2023.10059777 · Cited by 4
A survey on Machine Learning for IoT Trust Management
Akli, A., & Chougdali, K. (2023) · IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) · doi:10.1109/gcaiot61060.2023.10385118 · Cited by 4
Applicability of Machine Learning Algorithms for Intelligent Farming
Verma, B., Sharma, N., Kaushik, I., & Bhushan, B. (2021) · Studies in big data · doi:10.1007/978-3-030-75657-4_6 · Cited by 4
Role of AI and Big Data Analytics in UAV‐Enabled IoT Applications for Smart Cities
Wakode, M. S. (2021) · Wiley eBooks · doi:10.1002/9781119769170.ch11 · Cited by 4
Wang, X., Zhou, M., & Rosing, T. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3426308 · Cited by 3
Response Generation Honeypot With Antidetection Capabilities for IoT Botnet Lifecycle Detection
Tang, H., He, H. X., Feng, Y., Meng, J., & Zhang, W. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3509812 · Cited by 3
IOT botnet detection using transformer model and federated learning
Pavithran, D., Keloth, K. N. E., & Thankamani, R. N. (2025) · AIP conference proceedings · doi:10.1063/5.0247406 · Cited by 3
Anugraha, M., Ebenezer, S. S., & Maheswari, S. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102079 · Cited by 3
Sama, M., Sata, A. V., Joshi, G. R., & Mohan, D. G. (2025) · Engineering Reports · doi:10.1002/eng2.70257 · Cited by 3
A Machine Learning-Based Framework for Measuring Attack Surfaces of IoT Systems
Alshammari, B. M. (2025) · IEEE Access · doi:10.1109/access.2025.3593516 · Cited by 3
Madhushanie, N., Vidanagamachchi, S., & Arachchilage, N. A. G. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104659 · Cited by 3
Channel-Distortion-Agnostic Specific Emitter Identification Based on Decentralized Learning
Liu, Y., Xie, S., Sun, X., Fu, X. G., & Gui, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3612404 · Cited by 3
OptiXID: An optimized and eXplainable AI framework for intrusion detection in IoT networks
Albasheer, F. O., & Agarwal, M. (2025) · Cluster Computing · doi:10.1007/s10586-025-05777-0 · Cited by 3
Kong, S. C., Yang, Y. P., & Zhang, H. (2025) · Computers & Education X Reality · doi:10.1016/j.cexr.2025.100121 · Cited by 3
Kapoor, P., & Prasad, A. (2025) · International Journal of Computer Networks And Applications · doi:10.22247/ijcna/2025/43 · Cited by 3
MOAT: Federated Learning-Based Method for Improved Detection of IoT Botnets
Zhang, Y., Liu, W., & Zhu, T. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3631450 · Cited by 3
Data Science and Machine Learning for Cyber Intrusion Detection: A Systematic Review
Ren, Y. (2026) · Machine Learning Research · doi:10.11648/j.mlr.20261101.12 · Cited by 3
Kumar, A., Arif, H., Mazhar, T., Aldehim, G., Khan, M. A., & Hamam, H. (2026) · IET Networks · doi:10.1049/ntw2.70030 · Cited by 3
Zenia, M. M. (2026) · doi:10.63125/6kg7kg75 · Cited by 5
Eboya, O., & Juremi, J. B. (2021) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj060521 · Cited by 3
Abdel‐Basset, M., Moustafa, N., Hawash, H., & Ding, W. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-89025-4_1 · Cited by 3
Physical Layer Authentication Using Ensemble Learning Technique in燱ireless Communications
Waqas, M. M., Bano, S., Hassan, F., Tu, S., Abbas, G., & Abbas, Z. H. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.029539 · Cited by 3
A Data-Centric Anomaly-Based Detection System for Interactive Machine Learning Setups
Bugeja, J., & Persson, J. (2022) · International Conference on Web Information Systems and Technologies · doi:10.5220/0011560100003318 · Cited by 3
A Study on the Efficiency of Intrusion Detection Systems in IoT Networks
Seifi, S., Beaubrun, R., Bellaïche, M., & Halabi, T. (2023) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits58301.2023.10188799 · Cited by 3
SD6LoWPAN Security: Issues, Solutions, Research Challenges, and Trends
Moreira, C. M., & Kaddoum, G. (2023) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2200164 · Cited by 3
Maiwada, U. D., Danyaro, K. U., Sarlan, A. B., Liew, M. S., & Audi, U. I. (2023) · WSEAS TRANSACTIONS on COMMUNICATIONS archive · doi:10.37394/23204.2023.22.16 · Cited by 3
A method of classifying IoT devices based on attack sensitivity
Wang, H.-F., Guo, D., Wei, J., & Li, J. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103751 · Cited by 3
Cybersecurity Insights: Analyzing IoT Data Through Statistical and Visualization Techniques
Li, J., Othman, M. S., Chen, H., & Yusuf, L. M. (2024) · International Symposium on Parallel Computing and Distributed Systems (PCDS) · doi:10.1109/pcds61776.2024.10743769 · Cited by 3
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A Comprehensive Study of Deep Learning Models for Intrusion Detection in IoT Devices
Khairullah, E. F., & Alsenani, N. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.9490 · Cited by 3
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Zhang, S., Wu, D., Wang, H., & Wang, R. (2025) · IEEE Wireless Communications · doi:10.1109/mcom.005.2400490 · Cited by 3
Hybrid Deep Learning-Based Security Model for Robust Intrusion Detection in IoT Networks
Patil, J. J., & Solanki, R. (2025) · EPJ Web of Conferences · doi:10.1051/epjconf/202532801032 · Cited by 3
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Garg, U., Mishra, P., Gupta, N., & Pilli, E. S. (2025) · Cluster Computing · doi:10.1007/s10586-025-05633-1 · Cited by 3
Hussein, Z. A., & Naser, O. A. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100832 · Cited by 3
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Okatan, K. (2020) · Advances in data mining and database management book series · doi:10.4018/978-1-7998-2566-1.ch013 · Cited by 3
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Internet of Things Security Requirements, Threats, Attacks, and Countermeasures
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Bel, F., & Selvaraj, S. (2023) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i4.pp4605-4611 · Cited by 3
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Metadata
- Authors
- Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M.
- Affiliations
- Qatar University (QA)
- Temple University (US)
- University of Malaya (MY)
- ORCID
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 3
- Pages
- 1646-1685
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2885012953
- Semantic Scholar
- da8a949f9c9f1df3a38f12c2cac97b789c705465
- Cited by
- 1,392
- References
- 294
- Citations by year
- 2026: 170 · 2025: 234 · 2024: 232 · 2023: 237 · 2022: 213 · 2021: 217 · 2020: 75
- FWCI
- 87.17
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NPRP from the Qatar National Research Fund (8-408-2-172, 12S-0305-190231)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things security, machine learning for security, deep learning for security, IoT security threats, attack surface, IoT vulnerabilities, intrusion detection, anomaly detection, authentication, access control, network security
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