IoT anomaly detection methods and applications: A survey

Anomaly detection: A survey

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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

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Learning under Concept Drift: A Review

Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2876857 · Cited by 1,603

Deep Learning for Anomaly Detection: A Survey

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A Review on Outlier/Anomaly Detection in Time Series Data

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Anomaly Detection: A Survey

Musa, T. H. A., & Bouras, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2102-4_36 · Cited by 1,040

Anomaly Detection for IoT Time-Series Data: A Survey

Cook, A. A., Mısırlı, G., & Fan, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2958185 · Cited by 834

A Comprehensive Survey on TinyML

Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366

A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks

Inuwa, M. M., & Daş, R. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101162 · Cited by 191

A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges

Sasi, T., Lashkari, A. H., Lu, R., Xiong, P., & Iqbal, S. (2023) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2023.12.001 · Cited by 196

FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments

Rajagopal, S. M., Supriya, M., & Buyya, R. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100784 · Cited by 162

CVS-FLN: a novel IoT-IDS model based on metaheuristic feature selection and neural network classification model

Geetha, R., Jegatheesan, A., Dhanaraj, R. K., Vijayalakshmi, K., Nayyar, A., Arulkumar, V., Velmurugan, J., & Thavasimuthu, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19617-7 · Cited by 127

Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping

Trilles, S., Hammad, S. S., & Iskandaryan, D. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101063 · Cited by 118

Review of smart water management: IoT and AI in water and wastewater treatment

Dada, M. A., Majemite, M. T., Obaigbena, A., Daraojimba, O. H., Oliha, J. S., & Nwokediegwu, Z. Q. S. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.1.0171 · Cited by 98

Anomaly detection for space information networks: A survey of challenges, techniques, and future directions

Diro, A. A., Kaisar, S., Vasilakos, A. V., Anwar, A., Nasirian, A., & Ganfure, G. O. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103705 · Cited by 93

A Review of Anomaly Detection in Spacecraft Telemetry Data

Fejjari, A., Delavault, A., Camilleri, R., & Valentino, G. (2025) · Applied Sciences · doi:10.3390/app15105653 · Cited by 86

Anomaly Detection in Industrial Machinery Using IoT Devices and Machine Learning: A Systematic Mapping

Chevtchenko, S. F., Rocha, É. D. S., Santos, M. C. M. D., Mota, R. L., Vieira, D. M., Andrade, E., & Araújo, D. (2023) · IEEE Access · doi:10.1109/access.2023.3333242 · Cited by 85

Unsupervised Transformer-Based Anomaly Detection in ECG Signals

Alamr, A., & Artoli, A. M. (2023) · Algorithms · doi:10.3390/a16030152 · Cited by 71

Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives

Adhikari, D., Jiang, W., Zhan, J., Rawat, D. B., & Bhattarai, A. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100665 · Cited by 88

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Rahim, A., Zhong, Y., Ahmad, T., Ahmad, S., Pławiak, P., & Hammad, M. A. (2023) · Sensors · doi:10.3390/s23156979 · Cited by 67

Leveraging Machine Learning for Cybersecurity Resilience in Industry 4.0: Challenges and Future Directions

Yu, J., Shvetsov, A. V., & Alsamhi, S. H. (2024) · IEEE Access · doi:10.1109/access.2024.3482987 · Cited by 84

Stochastic gradient descent classifier-based lightweight intrusion detection systems using the efficient feature subsets of datasets

Azimjonov, J., & Kim, T. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121493 · Cited by 65

An efficient self attention-based 1D-CNN-LSTM network for IoT attack detection and identification using network traffic

Sasi, T., Lashkari, A. H., Lu, R., Xiong, P., & Iqbal, S. (2024) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2024.09.001 · Cited by 73

A deep dive into cybersecurity solutions for AI-driven IoT-enabled smart cities in advanced communication networks

Ali, J., Singh, S. K., Jiang, W., Alenezi, A. M., Islam, M., Daradkeh, Y. I., & Mehmood, A. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.108000 · Cited by 69

Blockchain-driven security for IoT networks: State-of-the-art, challenges and future directions

Maurya, V., Rishiwal, V., Yadav, M., Shiblee, M., Yadav, P., Agarwal, U., & Chaudhry, R. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01812-w · Cited by 64

Anomaly-based cyberattacks detection for smart homes: A systematic literature review

Iturbe-Araya, J.-I., & Rifà-Pous, H. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100792 · Cited by 65

A Hybrid CNN-LSTM Model With Attention Mechanism for Improved Intrusion Detection in Wireless IoT Sensor Networks

Phalaagae, P., Zungeru, A. M., Yahya, A., Sigweni, B., & Rajalakshmi, S. (2025) · IEEE Access · doi:10.1109/access.2025.3555861 · Cited by 50

Knowledge-based anomaly detection: Survey, challenges, and future directions

Khan, A. Q., Jaouhari, S. E., Tamani, N., & Mroueh, L. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108996 · Cited by 48

Securing IoT Networks Against DDoS Attacks: A Hybrid Deep Learning Approach

Ain, N. U., Sardaraz, M., Tahir, M., El-Soud, M. W. A., & Alourani, A. (2025) · Sensors · doi:10.3390/s25051346 · Cited by 46

Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System

Ramesh, J., Shahriar, S., Al-Ali, A. R., Osman, A. H., & Shaaban, M. F. (2022) · Energies · doi:10.3390/en15217981 · Cited by 45

A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study

Krzysztoń, E., Rojek, I., & Mikołajewski, D. (2024) · Applied Sciences · doi:10.3390/app142411545 · Cited by 44

Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

Maseer, Z. K., Kadhim, Q. K., Al‐Bander, B., Yusof, R., & Saif, A. (2024) · IET Networks · doi:10.1049/ntw2.12128 · Cited by 49

Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection

Chengula, T. J., Mwakalonge, J., Comert, G., Sulle, M., Siuhi, S., & Osei, E. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100580 · Cited by 35

A Survey on anomaly detection in IoT: Techniques, challenges, and opportunities with the integration of 6G

Haider, Z. A., Zeb, A., Rahman, T., Singh, S. K., Akram, R., Arishi, A., & Ullah, I. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111484 · Cited by 40

Using Machine Learning Multiclass Classification Technique to Detect IoT Attacks in Real Time

Alrefaei, A., & Ilyas, M. (2024) · Sensors · doi:10.3390/s24144516 · Cited by 34

Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey

Dritsas, E., & Τρίγκα, Μ. (2024) · Future Internet · doi:10.3390/fi16090324 · Cited by 42

Anomaly detection in encrypted network traffic using self-supervised learning

Sattar, S., Khan, S., Khan, M. I., Аkhmediyarova, A., Mamyrbayev, O. J., Kassymova, D., Оралбекова, Д., & Alimkulova, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-08568-0 · Cited by 34

Hybrid resampling and weighted majority voting for multi-class anomaly detection on imbalanced malware and network traffic data

Xue, L., & Zhu, T. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107568 · Cited by 36

Lightweight Signal Processing and Edge AI for Real-Time Anomaly Detection in IoT Sensor Networks

Reis, M. J. C. S. (2025) · Sensors · doi:10.3390/s25216629 · Cited by 32

A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor systems

Potharaju, S. P., Tirandasu, R. K., Tambe, S. N., Jadhav, D. B., kumar, D. A., & Amiripalli, S. S. (2025) · MethodsX · doi:10.1016/j.mex.2025.103181 · Cited by 31

A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application

Joha, M. I., Rahman, M. M., Nazim, M. S., & Jang, Y. M. (2024) · Sensors · doi:10.3390/s24237440 · Cited by 30

A Lightweight Small Object Detection Method Based on Multilayer Coordination Federated Intelligence for Coal Mine IoVT

Wu, J., Zheng, R., Jiang, J., Tian, Z., Chen, W., Wang, Z., Yu, F. R., & Leung, V. C. M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3373028 · Cited by 31

Anomaly detection system for data quality assurance in IoT infrastructures based on machine learning

Muñoz, L. A., Berná-Martínez, J. V., Pérez, F. M., & Fonseca, I. L. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101095 · Cited by 27

Improving smart home surveillance through YOLO model with transfer learning and quantization for enhanced accuracy and efficiency

Dalal, S., Lilhore, U. K., Sharma, N., Arora, S., Simaiya, S., Ayadi, M. F., Almujally, N. A., & Ksibi, A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1939 · Cited by 27

A survey on deep clustering: from the prior perspective

Lu, Y., Li, H., Li, Y., Lin, Y., & Peng, X. (2024) · Vicinagearth. · doi:10.1007/s44336-024-00001-w · Cited by 35

Anomaly detection for multivariate time series in IoT using discrete wavelet decomposition and dual graph attention networks

Xie, S., Li, L., & Zhu, Y. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104075 · Cited by 27

A survey on event detection approaches for sensor based IoT

Kumar, M., Singh, P. K., Maurya, M. K., & Shivhare, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100720 · Cited by 31

Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest

Pandey, V. K., Prakash, S., Gupta, T. K., Sinha, P., Yang, T., Rathore, R. S., Wang, L., Tahir, S., & Bakhsh, S. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-03498-3 · Cited by 31

MSDG: Multi-Scale Dynamic Graph Neural Network for Industrial Time Series Anomaly Detection

Zhao, Z., Xiao, Z. R., & Tao, J. (2024) · Sensors · doi:10.3390/s24227218 · Cited by 24

DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems

Zhao, M., & Fink, O. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3381002 · Cited by 29

SwinIoT: A Hierarchical Transformer-Based Framework for Behavioral Anomaly Detection in IoT-Driven Smart Cities

Mancy, H., & Naith, Q. H. (2025) · IEEE Access · doi:10.1109/access.2025.3551207 · Cited by 23

A deep learning framework for target localization in error-prone environment

Mohammed, S. K., Singh, S., Mizouni, R., & Otrok, H. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100713 · Cited by 23

Anomaly detection in IoT environment using machine learning

Bilakanti, H., Pasam, S., Palakollu, V., & Utukuru, S. (2024) · Security and Privacy · doi:10.1002/spy2.366 · Cited by 27

Dynamic behavioral profiling for anomaly detection in software-defined IoT networks: A machine learning approach

Senthilraja, P., Palaniappan, K., Brindha, D., & Balasubramanian, U. M. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01694-y · Cited by 22

Anomaly Detection for IOT Systems Using Active Learning

Zakariah, M., & Almazyad, A. S. (2023) · Applied Sciences · doi:10.3390/app132112029 · Cited by 22

Reconstruction error-based anomaly detection with few outlying examples

Angiulli, F., Fassetti, F., & Ferragina, L. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133002 · Cited by 36

Federated Learning With Sailfish-Optimized Ensemble Models for Anomaly Detection in IoT Edge Computing Environment

Babu, A., & Bagubali, A. (2025) · IEEE Access · doi:10.1109/access.2025.3554301 · Cited by 18

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Audi, U. I., Danyaro, K. U., Muazu, A. A., & Maiwada, U. D. (2024) · IEEE Access · doi:10.1109/access.2024.3369915 · Cited by 24

An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams

Mutambik, I. (2024) · Sensors · doi:10.3390/s24227412 · Cited by 18

Machine Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative Perspective of Cybersecurity and Cyberdefense in Colombia

Ortiz-Ruiz, E., Higuera, J. R. B., Montalvo, J. A. S., & Bermejo, J. (2024) · Electronics · doi:10.3390/electronics13050824 · Cited by 17

Building AI-Driven Cloud-Native Applications with Kubernetes and Containerization

Naayini, P. (2025) · International Journal Of Scientific Advances · doi:10.51542/ijscia.v6i2.15 · Cited by 16

Runtime Verification for Anomaly Detection of Robotic Systems Security

Kırca, Y. S., Değirmenci, E., Demirci, Z., Yazıcı, A. T., Özkan, M., Ergün, S., & Kanak, A. (2023) · Machines · doi:10.3390/machines11020166 · Cited by 16

A Process Mining-based unsupervised Anomaly Detection technique for the Industrial Internet of Things

Vitale, F., Vita, F. D., Mazzocca, N., & Bruneo, D. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100993 · Cited by 15

A bandwidth control scheme for reducing the negative impact of bottlenecks in IoT environments: Simulation and performance evaluation

Said, O. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100682 · Cited by 14

TinyIDS - An IoT Intrusion Detection System by Tiny Machine Learning

Fusco, P., Rimoli, G. P., & Ficco, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65223-3_5 · Cited by 14

Condition-based monitoring techniques and algorithms in 3d printing and additive manufacturing: a state-of-the-art review

Siddiqui, M. M. U. Z., & Tabassum, A. (2024) · Progress in Additive Manufacturing · doi:10.1007/s40964-024-00816-5 · Cited by 14

Deep Learning-Based Autonomous Anomaly Detection for Security in SDN-IoT Networks

Yasarathna, T. L., Liyanage, M., & Le‐Khac, N. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3610365 · Cited by 13

A Novel Few-Shot Deep Transfer Learning Method for Anomaly Detection: Deep Domain-Adversarial Contrastive Network With Time-Frequency Transferability Analytics

Wu, J., Mao, W., Zhang, Y., Fan, L., & Zhong, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3403976 · Cited by 13

An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework

Ghanim, J., & Awad, M. (2024) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2025-0001 · Cited by 14

A review of artificial intelligence impacting statistical process monitoring and future directions

Chang, S. I., Ghafariasl, P., & Aryal, B. (2026) · Computers & Industrial Engineering · doi:10.1016/j.cie.2026.111989 · Cited by 12

The development and validation of a milk feeding behavior alert from automated feeder data to classify calves at risk for a diarrhea bout: A diagnostic accuracy study

Cantor, M. C., Welk, A., Creutzinger, K. C., Setser, M. M. W., Costa, J. H. C., & Renaud, D. L. (2023) · Journal of Dairy Science · doi:10.3168/jds.2023-23635 · Cited by 12

Isolation Forest Algorithm Against UAV’s GPS Spoofing Attack

Mohammed, A. B., Fourati, L. C., & Fakhrudeen, A. M. (2024) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00090 · Cited by 12

Uncertainty-Aware Time Series Anomaly Detection

Wiessner, P., Bezirganyan, G., Sellami, S., Chbeir, R., & Bungartz, H. (2024) · Future Internet · doi:10.3390/fi16110403 · Cited by 19

Deep learning-based segmentation for medical data hiding with Galois field

Amrit, P., Singh, K. N., Baranwal, N., Singh, A. K., Singh, J. P., & Zhou, H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09151-2 · Cited by 11

Machine Learning Approaches in Blockchain Technology-Based IoT Security: An Investigation on Current Developments and Open Challenges

Hemashree, P., Kavitha, V., Mahalakshmi, S. B., Praveena, K., & Tarunika, R. (2024) · Signals and communication technology · doi:10.1007/978-3-031-49593-9_7 · Cited by 12

Advancements in Anomaly Detection Techniques in Network Traffic: The Role of Artificial Intelligence and Machine Learning

M, V. P. P., & Soumya, S. (2024) · Journal of Scientific Research and Technology · doi:10.61808/jsrt114 · Cited by 13

A Blockchain-Powered Malicious Node Detection in Internet of Autonomous Vehicles

Prathiba, S. B., Murali, P., Moorthy, R. S., Anandhan, D. K., Selvaraj, A. K., & Rodrigues, J. J. P. C. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3433480 · Cited by 11

A Review of Deep Packet Inspection for Network Security: From Traditional Techniques to Machine Learning Integration

Kumar, T., Leavy, S., Eustace, P., Curry, E., & Asghar, M. N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00639-4_11 · Cited by 11

Quality-Focused Internet of Things Data Management: A Survey, Perspectives, Open Issues, and Challenges

Abdalzaher, M. S., Krichen, M., Shaaban, M. F., & Fouda, M. M. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3613669 · Cited by 22

Anomaly Detection in IoT Environment Using Machine Learning: A Survey

Umar, A., & Jordanov, I. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3671102 · Cited by 11

Interpretable Single-dimension Outlier Detection (ISOD): An Unsupervised Outlier Detection Method Based on Quantiles and Skewness Coefficients

Huang, Y., Liu, W., Li, S., Guo, Y., & Chen, W. (2023) · Applied Sciences · doi:10.3390/app14010136 · Cited by 10

Two-Phase Dual-Adversarial Agents With Multivariate Information for Unsupervised Anomaly Detection of IIoT-Edge Devices

Chang, Y., Chen, J., Su, R., Xie, J., & Li, A. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3385278 · Cited by 10

Concept-drift-adaptive anomaly detector for marine sensor data streams

Nguyen, N.-T., Heldal, R., & Pelliccione, P. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101414 · Cited by 10

Novel R-C-C fusion machine learning method for automatic damage detection of heritage buildings using 3D point cloud data

Seo, H., Sihag, P., Fu, L., & Kim, D. (2025) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-025-00918-w · Cited by 9

Optimising Manufacturing Efficiency: A Data Analytics Solution for Machine Utilisation and Production Insights

Seyedzadeh, S., Christodoulou, V., Turner, A., & Lotfian, S. (2025) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp9070210 · Cited by 9

Current Challenges and Future Perspectives in Testing IoT Systems: A Comprehensive Review

Lima, B., & Pinto, R. (2025) · IEEE Sensors Reviews · doi:10.1109/sr.2025.3628264 · Cited by 9

AEDS-IoT: Adaptive clustering-based Event Detection Scheme for IoT data streams

Raut, A., Shivhare, A., Chaurasiya, V. K., & Kumar, M. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100704 · Cited by 9

Exploring the Potential of Cyber Manufacturing System in the Digital Age

Ahmed, U., Lin, J. C., & Srivastava, G. (2023) · ACM Transactions on Internet Technology · doi:10.1145/3596602 · Cited by 9

Matyas–Meyer Oseas based device profiling for anomaly detection via deep reinforcement learning (MMODPAD-DRL) in zero trust security network

Dhanaraj, R. K., Singh, A., & Nayyar, A. (2024) · Computing · doi:10.1007/s00607-024-01269-y · Cited by 9

Untitled work

(2025) · doi:10.1016/j.iot.2026.101938 · Cited by 31

Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization

Salimpour, S., Morón, P. T., Yu, X., Westerlund, T., & Peña-Queralta, J. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1190296 · Cited by 8

KBJNet: Kinematic Bi-Joint Temporal Convolutional Network Attention for Anomaly Detection in Multivariate Time Series Data

Mulia, M. A., Bahy, M. B., Siswantoro, M. Z. F. N., Riyanto, N. R. D., Sudianjaya, N. R., & Shiddiqi, A. M. (2024) · Data Science Journal · doi:10.5334/dsj-2024-010 · Cited by 9

On data efficiency of univariate time series anomaly detection models

Sun, W., Li, H., Liang, Q., Zou, X., Chen, M., & Wang, Y. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00940-7 · Cited by 8

Edge Multi-agent Intrusion Detection System Architecture for IoT Devices with Cloud Continuum

Funchal, G., Pedrosa, T., la Prieta, F. D., & Leitão, P. (2024) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps59941.2024.10639952 · Cited by 8

ML-Based Anomaly Detection in 6G Networks: A Survey on the Current Status, Challenges, and Future Directions

Nezhadsistani, N., & Stiller, B. (2024) · International Conference on 6G Networking (6GNet) · doi:10.1109/6gnet63182.2024.10765631 · Cited by 8

Enhancing Construction Waste Transportation Management Using Internet of Things (IoT): An Evaluation Framework Based on AHP–FCE Method

Ateeq, M., Zhang, N., Zhao, W., Gu, Y., Wen, Z., Zheng, C., & Hao, J. L. (2025) · Buildings · doi:10.3390/buildings15081381 · Cited by 10

Enhancing IoT Resilience at the Edge: A Resource-Efficient Framework for Real-Time Anomaly Detection in Streaming Data

Kirubavathi, G., Pulliyasseri, A., Rajesh, A., Ajayan, A., Alfarhood, S., Safran, M., Alfarhood, M., & Shin, J. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.065698 · Cited by 8

Edge-FLGuard+: A Federated and Lightweight Anomaly Detection Framework for Securing 5G-Enabled IoT in Smart Homes

Reis, M. J. C. S. (2025) · Future Internet · doi:10.3390/fi17080329 · Cited by 8

Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

Domb, M., Joshi, S., & Khn, A. (2023) · Artificial intelligence · doi:10.5772/intechopen.111944 · Cited by 8

ARAD: Automated and Real-Time Anomaly Detection in Sensors of Autonomous Vehicles Through a Lightweight Supervised Learning Approach

Abdi, A., & Ghasemi-Tabar, A. (2024) · IEEE Access · doi:10.1109/access.2024.3420090 · Cited by 8

Machine Learning Algorithms for Intrusion Detection in IoT-enabled Smart Homes

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Success Factors of IoT Security: A Structured Analysis Using Fuzzy-AHP

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