Cited by 102Network Security and Intrusion DetectionSpam and Phishing DetectionInternet of Things and AI

The DOI matches its Crossref record; 104 of the 106 references it cites were found in a registry or an index.

Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

Open access

Wazzan, Majda, Algazzawi, Daniyal, Bamasaq, Omaima, Albeshri, Aiiad, Cheng, Li·2021·Applied Sciences

Open this paper in CoChat — free account Open PDF

Abstract

Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cybersecurity risks due to the lack of security for IoT devices. IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analyzing the importance of botnet detection methods are limited in the IoT environment. Thus, this study aimed to identify, assess and provide a thoroughly review of experimental works on the research relevant to the detection of IoT botnets. To accomplish this goal, a systematic literature review (SLR), an effective method, was applied for gathering and critically reviewing research papers. This work employed three research questions on the detection methods used to detect IoT botnets, the botnet phases and the different malicious activity scenarios. The authors analyzed the nominated research and the key methods related to them. The detection methods have been classified based on the techniques used, and the authors investigated the botnet phases during which detection is accomplished. This research procedure was used to create a source of foundational knowledge of IoT botnet detection methods. As a result of this study, the authors analyzed the current research gaps and suggest future research directions.

Integrity signals

Registry

Crossref · 10.3390/app11125713

Cites

104 of 104 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

1 retracted

RETRACTED: Security challenges in internet of things: Distributed denial of service attack detection using support vector machine‐based expert systems
Mubarakali, A., Srinivasan, K., Mukhalid, R., Jaganathan, S. C. B., & Marina, N. (2020) · Computational Intelligence · doi:10.1111/coin.12293 · Cited by 36
Retracted
Performing systematic literature reviews in software engineering
Budgen, D., & Brereton, P. (2006) · International Conference on Software Engineering · doi:10.1145/1134285.1134500 · Cited by 8,824
Systematic Reviews in the Social Sciences: A Practical Guide
Petticrew, M., & Roberts, H. (2006) · doi:10.1002/9780470754887 · Cited by 3,586
That ‘Internet of Things’ Thing
Ashton, K. J. (1999) · Medical Entomology and Zoology · Cited by 2,581
IoT security: Review, blockchain solutions, and open challenges
Khan, M. A., & Salah, K. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.11.022 · Cited by 2,488
Lessons from Applying the Systematic Literature Review Process within the Software Engineering Domain
Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007) · Journal of Systems and Software · doi:10.1016/j.jss.2006.07.009 · Cited by 2,394
Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset
Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.041 · Cited by 1,983
N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018) · IEEE Pervasive Computing · doi:10.1109/mprv.2018.03367731 · Cited by 1,668
A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures
Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P., & Sikdar, B. (2019) · IEEE Access · doi:10.1109/access.2019.2924045 · Cited by 1,445
A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT
Sengupta, J., Ruj, S., & Das Bit, S. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102481 · Cited by 844

Flagged first, then by citations

Show all 106 references

2 of the 106 references couldn’t be matched to a known paper.

Research works printed but not linked: 7

Alhajri, 2019, Survey for anomaly detection of IoT botnets using machine learning auto-encoders, Int. J. Appl. Eng. Res., 14, 2417
De Donno, M., Dragoni, N., Giaretta, A., and Spognardi, A. (2017, January 3–6). Analysis of DDoS-capable IoT malwares. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic.
Manos, A., April, T., Bailey, M., Bernhard, M., Bursztein, E., Cochran, J., and Durumeric, Z. (2017, January 16–18). Un-derstanding the mirai botnet. Proceedings of the 26th {USENIX} security symposium ({USENIX} Security 17), Vancouver, BC, Canada.
Costin, A., and Zaddach, J. (2018, January 3–6). Iot malware: Comprehensive survey, analysis framework and case studies. Proceedings of the BlackHat, Las Vegas, NV, USA.
Edwards, 2016, Hajime: Analysis of a decentralized internet worm for IoT devices, Rapidity Netw., 16, 1
Kamal, M., Aljohani, A., and Alanazi, E. (2007). IoT meets COVID-19: Status, challenges, and opportunities. arXiv.
Beek, C. (2021, March 05). McAfee Labs Covide-19 Threat Report. Available online: https://www.mcafee.com/enterprise/en-us/assets/reports/rp-quarterly-threats-july-2020.pdf.

Sorted by citations

Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets
Nazir, A., He, J., Zhu, N., Wajahat, A., Ma, X., Ullah, F., Qureshi, S., & Pathan, M. S. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101820 · Cited by 134
Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review
Al-Shurbaji, T., Anbar, M., Manickam, S., Hasbullah, I. H., Alfriehat, N. A., Alabsi, B. A., Alzighaibi, A. R., & Hashim, H. A. (2025) · IEEE Access · doi:10.1109/access.2025.3526711 · Cited by 129
A Review of IoT Firmware Vulnerabilities and Auditing Techniques
Bakhshi, T., Ghita, B., & Kuzminykh, I. (2024) · Sensors · doi:10.3390/s24020708 · Cited by 89
BotStop : Packet-based efficient and explainable IoT botnet detection using machine learning
Alani, M. M. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.06.039 · Cited by 79
A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
Vu, S. N. T., Stege, M., El-Habr, P. I., Bang, J., & Dragoni, N. (2021) · Future Internet · doi:10.3390/fi13080198 · Cited by 62
Botnet‐based IoT network traffic analysis using deep learning
Singh, N. J., Hoque, N., Singh, K. R., & Bhattacharyya, D. K. (2023) · Security and Privacy · doi:10.1002/spy2.355 · Cited by 57
Energy-based approach for attack detection in IoT devices: A survey
Merlino, V., & Allegra, D. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101306 · Cited by 46
Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning
Negera, W. G., Schwenker, F., Debelee, T. G., Melaku, H. M., & Ayano, Y. M. (2022) · Sensors · doi:10.3390/s22249837 · Cited by 35
IoT malware classification based on reinterpreted function-call graphs
Wu, C., Ban, T., Cheng, S., Takahashi, T., & Inoue, D. (2022) · Computers & Security · doi:10.1016/j.cose.2022.103060 · Cited by 31
Detecting Internet of Things Bots: A Comparative Study
Stephens, B., Shaghaghi, A., Doss, R., & Kanhere, S. S. (2021) · IEEE Access · doi:10.1109/access.2021.3130714 · Cited by 27
Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets
Ullah, S., Wu, J., Lin, Z., Kamal, M. M., Mostafa, H., Sheraz, M., & Chuah, T. C. (2025) · Scientific Reports · doi:10.1038/s41598-025-16553-w · Cited by 26
Enhanced botnet detection in IoT networks using zebra optimization and dual-channel GAN classification
Shareef, S. K., Chaitanya, R. K., Chennupalli, S., Chokkakula, D., Kiran, K. V. D., Udayaraju, P., & Vatambeti, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-67865-2 · Cited by 24
Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection
Mutambik, I. (2024) · Applied Sciences · doi:10.3390/app14219848 · Cited by 27
Developing a hybrid feature selection method to detect botnet attacks in IoT devices
Alshaeaa, H. Y., & Ghadhban, Z. M. (2024) · Kuwait Journal of Science · doi:10.1016/j.kjs.2024.100222 · Cited by 23
A novel feature selection-driven ensemble learning approach for accurate botnet attack detection
Hossain, M. A., & Islam, M. S. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.01.042 · Cited by 22
A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology
Khan, Z. A., & Namin, A. S. (2022) · Electronics · doi:10.3390/electronics11233892 · Cited by 22
BOTA: Explainable IoT Malware Detection in Large Networks
Uhříček, D., Hynek, K., Čejka, T., & Kolář, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3228816 · Cited by 24
Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet
Wazzan, M., Algazzawi, D., Albeshri, A. A., Hasan, S. H., Rabie, O. B. J., & Asghar, M. Z. (2022) · Sensors · doi:10.3390/s22103895 · Cited by 21
Self-citation
A Lightweight Method for Botnet Detection in Internet of Things Environment
Ma, W., Wang, X., Dong, J., Hu, M., & Zhou, Q. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3548411 · Cited by 21
Edge security for SIP-enabled IoT devices with P4
Febro, A., Xiao, H., Spring, J., & Christianson, B. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108698 · Cited by 19
Internet of Things botnets: A survey on Artificial Intelligence based detection techniques
Lefoane, M., Ghafir, I., Kabir, S., & Awan, I. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104110 · Cited by 18
A threat modeling framework for IoT-Based botnet attacks
Jin, H., Jeon, G., Choi, H. W. A., Jeon, S., & Seo, J. T. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39192 · Cited by 19
Uncovering botnets in IoT sensor networks: a hybrid self-organizing maps approach
Abu-Alhaija, M., Alqudah, H. J., & Dar-Othman, H. (2024) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v34.i3.pp1840-1857 · Cited by 17
Machine Learning Approaches for Botnet Detection in Network Traffic
Salih, Y. T., Fenjan, A., Ahmed, S. R., Ali, H., Abdulwahab, E. N., Algruri, S., Kurdi, N. A., Al-Sarem, M., & Tawfeq, J. F. (2024) · Cognitive Models and Artificial Intelligence Conference (AICCONF) · doi:10.1145/3660853.3660933 · Cited by 17
AI-based botnet attack classification and detection in IoT devices
Puri, V., Kataria, A., Solanki, V. K., & Rani, S. (2022) · IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) · doi:10.1109/icmlant56191.2022.9996464 · Cited by 17
Internet of Things (IoT) Security Enhancement Using XGboost Machine Learning Techniques
Doghramachi, D. F., & Ameen, S. Y. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.041186 · Cited by 17
IoT Botnet Attack Detection Model Based on DBO-Catboost
Yang, C., Guan, W., & Fang, Z. (2023) · Applied Sciences · doi:10.3390/app13127169 · Cited by 16
Insect-based organic waste management: a sustainable pathway to enhanced ecosystem services and food security
Ayompe, L. M., Masso, C., Epie, W. N., Crook, E. D., & Egoh, B. N. (2025) · Frontiers in Sustainability · doi:10.3389/frsus.2025.1620925 · Cited by 16
IoT Malware Detection Using Function-Call-Graph Embedding
Wu, C.-Y., Ban, T., Cheng, S., Sun, B., & Takahashi, T. (2021) · Conference on Privacy, Security and Trust · doi:10.1109/pst52912.2021.9647806 · Cited by 14
Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method
Rafrastara, F. A., Supriyanto, C., Paramita, C., Astuti, Y. P., & Ahmed, F. (2023) · Jurnal Informatika Jurnal Pengembangan IT · doi:10.30591/jpit.v8i2.5207 · Cited by 13
A Hybrid Technique Based on RF-PCA and ANN for Detecting DDoS Attacks IoT
Jalo, H. N., & Heydarian, M. (2024) · Iraqi journal of data science. · doi:10.51173/ijds.v1i1.9 · Cited by 12
SHAP-based intrusion detection in IoT networks using quantum neural networks on IonQ hardware
K, R. K., & Shalinie, S. M. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105133 · Cited by 12
Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System
Pan, X., & Yamaguchi, S. (2022) · Sensors · doi:10.3390/s22134666 · Cited by 11
Detection of HTTP DDoS Attacks Using NFStream and TensorFlow
Chovanec, M., Hasin, M., Havrilla, M., & Chovancová, E. (2023) · Applied Sciences · doi:10.3390/app13116671 · Cited by 11
Modular neural network for edge-based detection of early-stage IoT botnet
Alqattan, D. S., Ojha, V., Habib, F., Noor, A., Morgan, G., & Ranjan, R. K. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100230 · Cited by 10
Your IP Camera Can Be Abused for Payments: A Study of IoT Exploitation for Financial Services Leveraging Shodan and Criminal Infrastructures
Siwakoti, Y. R., Bhurtel, M., Rawat, D. B., Oest, A., & Johnson, R. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3482708 · Cited by 9
Transformers model for DDoS attack detection: A survey
Farias, E. P., de Neira, A. B., Borges, L. F., & Nogueira, M. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111433 · Cited by 13
Adaptive DBSCAN with Grey Wolf Optimizer for Botnet Detection
(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.0831.33 · Cited by 9
An approach to botnet attacks in the fog computing layer and Apache Spark for smart cities
Dawi, A. A., Tezel, N. S., Rahebi, J., & Akbaş, A. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-024-06915-y · Cited by 8
A Systematic Literature Review: Classifying IoT Botnet Data Features Based on Its Lifecycle
Liu, S., Fauzi, F., & Kok, V. J. (2025) · IEEE Access · doi:10.1109/access.2025.3559635 · Cited by 8
A Proposed Framework for Early Detection IoT Botnet
Mashaleh, A. S., Ibrahim, N. F., Alauthman, M., & Almomani, A. (2022) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit57182.2022.9994166 · Cited by 8
MONDEO-Tactics5G: Multistage botnet detection and tactics for 5G/6G networks
Sousa, B., Dias, D., Antunes, N., Cámara, J., Wagner, R., Schmerl, B. R., Garlan, D., & Fidalgo, P. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103768 · Cited by 7
IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with燚eep燣earning
Alshahrani, S. M., Alrayes, F. S., Alqahtani, H., Alzahrani, J. S., Maray, M., Alazwari, S., Shamseldin, M. Α., & Duhayyim, M. A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.032972 · Cited by 7
Evaluation of machine learning and deep learning methods for early detection of internet of things botnets
Mashaleh, A. S., Ibrahim, N. F., Alauthman, M., Al-Karaki, J. N., Almomani, A., Atalla, S., & Gawanmeh, A. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i4.pp4732-4744 · Cited by 6
Detecting IoT Botnet in 5G Core Network Using Machine Learning
Kim, Y.-E., Kim, M.-G., & Kim, H. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026581 · Cited by 6
A Comprehensive Review of Cybersecurity Threats to Wireless Infocommunications in the Quantum-Age Cryptography
Laktionov, І., Diachenko, G., Moroz, D. M., & Getman, I. A. (2025) · IoT · doi:10.3390/iot6040061 · Cited by 5
Constructing IoT Botnet Detection Model Based on Degree Centrality and Path Analysis
Zaki, W. N. F. W. M., Abdullah, R. S., Yassin, W. M., Selamat, S. R., Rosli, M. S., & Yahya, S. (2024) · Journal of Advances in Information Technology · doi:10.12720/jait.15.3.330-339 · Cited by 5
Efficient Hybrid Model for Botnet Detection Using Machine Learning
Saxena, P., & Patel, R. B. (2023) · International Conference on Computation, Automation and Knowledge Management (ICCAKM) · doi:10.1109/iccakm58659.2023.10449643 · Cited by 5
Feature selection for IoT botnet detection using equilibrium and Battle Royale Optimization
Baker, Q. B., & Samarneh, A. A. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104060 · Cited by 5
Adapting to the Evolution: Enhancing Intrusion Detection Through Machine Learning in the QUIC Protocol Era
Kadi, A., Khoukhi, L., Viinikka, J., & Fabre, P.-E. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3540753 · Cited by 5
The Applications of Blockchains in Addressing the Integration and Security of IoT Systems: A Survey
Khan, Z. A., & Namin, A. S. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671299 · Cited by 6
Integration of blockchain technology into smart cities for information security: Research constituents and need of research
Sharma, S., & Mishra, N. (2026) · AIP conference proceedings · doi:10.1063/5.0286235 · Cited by 5
Deep Learning-Based Detection of IoT Botnet Attacks: An Exploration of Residual Networks
Govindaraji, M., Periyasamy, R., & Vidyaathulasiraman (2023) · International Journal of Safety and Security Engineering · doi:10.18280/ijsse.130414 · Cited by 4
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
Attacks Come to Those Who Wait: Long-Term Observations in an SSH Honeynet
Munteanu, C., Suriyanarayanan, Y. B., Smaragdakis, G., Feldmann, A., & Fiebig, T. (2025) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3730567.3764475 · Cited by 3
Iot botnet intrusion detection system using federated learning techniques
Saleh, A., Alkasassbeh, M. S., & Almseidin, M. (2026) · Wireless Networks · doi:10.1007/s11276-026-04100-y · Cited by 3
Smart Botnet Detection in IoT using Stacked Neural Networks
Chaitanya, V. L., Reddy, P. S., Devi, M. S., Reddy, M. S. K., Vardhan, P. V., & Rao, J. L. (2026) · International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) · doi:10.1109/icauc68182.2026.11440982 · Cited by 3
Hash-Based Function Call Graph Fusion Method for IoT Malware Detection
Lin, G.-Y., Lin, M.-H., Hong, B.-K., & Cheng, S. (2023) · International Symposium on Wireless Personal Multimedia Communications (WPMC) · doi:10.1109/wpmc59531.2023.10338981 · Cited by 3
Traffic Classification For Botnet Detection Using Deep Learning
Hossen, M. S., & Ahmad, T. (2023) · International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS) · doi:10.1109/icon-sonics59898.2023.10435270 · Cited by 3
Hierarchical Classification of Botnet Using Lightweight CNN
Negera, W. G., Schwenker, F., Feyisa, D. W., Debelee, T. G., & Melaku, H. M. (2024) · Applied Sciences · doi:10.3390/app14103966 · Cited by 3
A CNN based deep learning model for detecting P2P Botnets using flow features
Mohan, H. G., Kumar, J., Rajesh, I. S., Geethanjali, K. S., Krishnamurthy, M. S., & Jagannathan, K. (2024) · International Conference on Networks, Multimedia and Information Technology (NMITCON) · doi:10.1109/nmitcon62075.2024.10698766 · Cited by 3
Detection and Mitigation in IoT Ecosystems Using oneM2M Architecture and Edge-Based Machine Learning
Luo, Y.-Y., Chiu, Y.-H., & Cheng, C.-H. (2025) · Future Internet · doi:10.3390/fi17090411 · Cited by 3
Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap
Nageshwaran, V., & Ezekiel, S. (2026) · Electronics · doi:10.3390/electronics15122740 · Cited by 4
IoT botnets Unveiled: architectural analysis, threat vectors, and cutting-edge detection techniques
Garg, U., Mishra, P., Gupta, N., & Pilli, E. S. (2025) · Cluster Computing · doi:10.1007/s10586-025-05633-1 · Cited by 3
Efficient Scanning Activity Detection in IoT Networks Using Ensemble Learning
Igried, B., Alauthman, M., Khaldy, M. A. A., & Alsarhan, A. (2023) · Asia Conference on Artificial Intelligence, Machine Learning and Robotics (AIMLR) · doi:10.1145/3625343.3625346 · Cited by 2
Detecting Iot Botnet Attacks: A Machine Learning Paradigm
Purnachandrarao, M., Sushma, K., Anantha, M., Reddy, S. A., Nithin, M., & Ranjithkumar, V. R. (2024) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp60870.2024.10543288 · Cited by 2
A Review of Backdoor Control Techniques for Embedded Devices
Chen, S., Guang, Y., & Han, Q. (2024) · International Conference on Information Technology and Computer Communications (ITCC) · doi:10.1145/3704391.3704392 · Cited by 2
Exploring Machine Learning Approaches for Botnet Detection in IoT Networks: A Review
Rasool, A. M., Safa, N. S., & Mbarushimana, C. (2025) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-82031-1_9 · Cited by 2
Improving spam botnet detection through convolutional model and geolocation feature enhancement in a novel three-class classification task
Benedictus, F., Putra, M. A. R., Ahmad, T., Za’in, C., & de Souza-Daw, T. (2025) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2025.08.001 · Cited by 2
On the Use of Low-Cost IoT Devices to Perpetrate Slow DoS Attacks
Cambiaso, E. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3043-2_71 · Cited by 2
Research on botnet detection technology in network security
Yu, H. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/18/20230967 · Cited by 2
Botnets in Healthcare: Threats, Vulnerabilities, and Mitigation Strategies
Barnett, M., Womack, J., Brito, C., Miller, K., Potter, L. N., & Palmer, X. (2024) · European Conference on Cyber Warfare and Security · doi:10.34190/eccws.23.1.2345 · Cited by 2
A Comprehensive Review of Internet-of-Things (IoT) Botnet Detection Techniques
Nketia, I. K., Yaokumah, W., & Appati, J. K. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-3451-5.ch003 · Cited by 1
Ensemble Learning Techniques for the Detection of IoT Botnets
Nazir, A., He, J., Zhu, N., Ma, X., Ullah, F., Qureshi, S., & Wajahat, A. (2024) · International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE) · doi:10.1145/3672919.3672934 · Cited by 2
Exploring Deep Learning Architectures for Enhanced Cyber Threat Detection: A Survey
Oyinloye, T. S., Arowolo, M. O., Ogundoku, R. O., & Prasad, R. (2024) · International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) · doi:10.1109/seb4sdg60871.2024.10629849 · Cited by 1
Quantum-Enhanced IoT-Cloud Security: Integrating SHAP and Variational Quantum Classifiers
Antony, V., Antony, V., & Thangarasu, N. (2025) · Journal of Computer Science · doi:10.3844/jcssp.2025.168.176 · Cited by 1
Combating evolving threats: A robust malicious domain detection framework for distributed networks
Jin, Y., He, F., Zhu, Y., & Ren, H. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02036-2 · Cited by 1
Leveraging Random Forest to Detect Botnet Attacks in IoT Environments
Alrakah, H., Adam, Y. A., Abdalraheem, M. H. O., Mansur, P., Rizwan, S. M., & Al–Shourbaji, I. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.3717 · Cited by 1
Botnet Detection in IoT Networks Using Hybrid Deep Learning CNN, LSTM, and GRU
Adil, A. S. S., & Hoomod, H. K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14189-7_27 · Cited by 1
Security Challenges and Mitigation Strategies for Internet of Medical Things (IoMT): A Cross-Layer Analysis of Embedded Systems and Networked Environments
El-Taj, H. R., & Hamarsheh, A. (2025) · International Conference on Electrical and Computer Engineering Researches (ICECER) · doi:10.1109/icecer65523.2025.11401305 · Cited by 1
A Survey on IOT Botnets and their Detection Approaches
Yadav, R. K., & Karamveer, K. (2022) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n56670.2022.10074482 · Cited by 2
A review of machine learning techniques and challenges in online Botnet detection
Haidar, D., & Husien, I. M. (2025) · AIP conference proceedings · doi:10.1063/5.0274147 · Cited by 1
Analysis of Data Obtained from the Mobile Botnet
Kobiela, J., & Urbaniec, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48348-6_2
Neglected Cyber Threats on the Rise: Combatting IoT Botnet Infection Techniques through the Elucidation of Machine Learning
Blancaflor, E., Evangelista, R. C. B., Maligmat, R. R., Marcelo, D. N. B., Pablo, B. J. P., & Vilar, M. J. B. (2023) · International Conference on Computing and Big Data (ICCBD) · doi:10.1109/iccbd59843.2023.10607336
Advancing Immune Disorder Prediction on a Machine Learning Approach for Enhanced Accuracy
Basha, S. K. A., & Hanirex, D. K. (2024) · Advances in computer and electrical engineering book series · doi:10.4018/979-8-3693-3739-4.ch022
Securing Cyberspace: Traditional vs. IoT Botnets-A Machine Learning Classification Approach
Gupta, S., Alexander, J., Bartwal, K., Narang, H., Rawat, D., & Aeri, M. (2024) · International Conference on Advanced Computing and Emerging Technologies (ACET) · doi:10.1109/acet61898.2024.10730487
Towards Establishing the Role of LLMs in Botnet Detection: Effective Prompts for Source Code Analysis
Miller, S. T., & Busby-Earle, C. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-73128-0_18
Performance Evaluation of Botnet Attack Detection Using XAI
Divakarla, U., & Chandrasekaran, K. (2024) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp61132.2024.10752294
Integrating Deep Learning with IOT: Combined Strategies for Botnet Detection
Soni, S. K., & Nair, S. (2024) · SMART MOVES JOURNAL IJOSCIENCE · doi:10.24113/ijoscience.v10i9.487
An Ensemble Rule Extraction Algorithm Based on Interpretable Greedy Trees for Detecting Malicious Traffic*
Ku, Q., Tie-jun, W., & Ya, Z. (2024) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv63323.2024.10821513
Distributed BERT-based Detection Systems for IoT Networks Against Coordinated Bot Attacks
Ayesha Siddhika, A., Balamurugan, P., Logeshwaran, C., & Subalaxmi, T. (2025) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs65191.2025.10985487
Detecting IoT Botnet Attacks Using Explainable Artificial Intelligence (XAI) with Decision Tree and Random Forest
Shiddiqi, A. M., Purba, R. C., & Pratomo, B. A. (2025) · International Conference on Smart Computing, IoT and Machine Learning (SIML) · doi:10.1109/siml65326.2025.11080918
Lightweight Edge Computing Framework for IoT Botnet Detection in Dynamic Environment
Assistant, M. T., Sivanesh, C., Kumar, M. S., & Dharineesh, S. (2025) · International Conference on Emerging Applications of Material Science and Technology (ICEAMST) · doi:10.1109/iceamst67459.2025.11335881
Botnet Attacks Detection in IoT Smart Homes: A Machine Learning Approach
Ali, H. A. S., Rani, J. V., & Challa, M. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-9716-8_12
Advancements in AI-Based Botnet Detection Techniques for IoT Networks: A Comprehensive Survey
Das, R., Deka, V., Devi, R., Dey, A., Sharma, M., & Taye, G. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9935-3_11
A Hybrid Rule-Based and Machine Learning Approach for Distributed Denial of Service (DDoS) Attack Detection and Mitigation in Wireless Sensor Network–Enabled Smart Healthcare Systems
Singh, P. B., & Swami, S. (2026) · Cureus Journal of Computer Science. · doi:10.7759/s44389-026-00030-0
Evaluation of Blockchain Technology for Enhancing Data Privacy and Security in Cloud Computing Environments
Alkhattabi, K. (2026) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194026410020
Enhancing Security in IoT Networks by Applying Deep Learning Approaches for Accurate and Efficient Botnet Detection
Dubey, J., & Patel, D. (2025) · International Conference on Data, Energy and Communication Networks (DECoN) · doi:10.1109/decon67170.2025.11447625
Edge-AI Empowered Framework for Real-Time Botnet Detection in IoT Ecosystems using Distributed Machine Learning
Mohanraj, T., Sivanesh, C., Sanjeev Kumar, M., & Dharineesh, S. (2025) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss64956.2025.11501158
Regulating Automated Fraud
Jyoti, Prajapati, N., Kulshreshtha, P., & Ghosh, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7660-8.ch009

Authors: Wazzan, M., Algazzawi, D., Bamasaq, O., Albeshri, A., & Cheng, L.
Affiliations: King Abdulaziz University (SA)
Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Chinese Academy of Sciences (CN)
Corresponding author: Wazzan, Majda
ORCID: Wazzan, Majda
Algazzawi, Daniyal
Albeshri, Aiiad
Cheng, Li
Venue: Applied Sciences
Publisher: MDPI AG
Published: 2021-06-20
Volume: 11
Issue: 12
Pages: 5713
ISSN: 2076-3417 (ISSN-L)
DOI: 10.3390/app11125713
OpenAlex: W3175402960
Cited by: 102
References: 106
Citations by year: 2026: 12 · 2025: 27 · 2024: 30 · 2023: 16 · 2022: 12 · 2021: 5
FWCI: 7.07
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Applied Sciences (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: King Abdulaziz University
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords: IoT botnets, botnet detection, Internet of Things security, IoT device security, research gaps, future research directions

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks