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

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N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders

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Learning under Concept Drift: A Review

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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

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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

Enhancing Internet of Smart City Security: Utilizing Logistic Boosted Algorithms for Anomaly Detection and Cyberattack Prevention

Al‐Atawi, A. A. (2024) · SN Computer Science · doi:10.1007/s42979-024-02921-2 · Cited by 7

Hybrid Ensemble Learning with Explainable AI for Anomaly Detection in Network Traffic

Gupta, A., Phulre, A. K., Patel, A. M., & Rahman, R. U. (2024) · International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC) · doi:10.1109/icicec62498.2024.10808834 · Cited by 7

Anomaly Detection for Hydraulic Power Units—A Case Study

Fic, P., Czornik, A., & Rosikowski, P. (2023) · Future Internet · doi:10.3390/fi15060206 · Cited by 7

IoT Security, Future Challenges, and Open Issues

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Integrated analysis of reliability, power, and performance for IoT devices and servers

Li, K. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103216 · Cited by 7

Computer-aided urban energy systems cyber attach detection and mitigation: Intelligence hybrid machine learning technique for security enhancement of smart cities

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Improving adversarial resilience for anomaly detection in the heterogeneous internet of things through ensemble models

Abiha, U.-E., Rehman, A., Abbas, A., Haider, M. A., Al‐Yarimi, F. A. M., Gul, M. U., & Hassan, S. R. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108299 · Cited by 6

Addressing the Faults Landscape in the Internet of Things: Toward Datacentric and System Resilience

Altarrazi, S. M., Szydło, T., Dustdar, S., Srirama, S. N., & Ranjan, R. K. (2023) · IEEE Internet Computing · doi:10.1109/mic.2023.3300508 · Cited by 6

A survey of data mining methodologies in the environment of IoT and its variants

Marshoodulla, S. Z., & Saha, G. K. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103907 · Cited by 8

Empirical study of outlier impact in classification context

Khan, H., Rasheed, M. T., Zhang, S., Wang, X., & Liu, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124953 · Cited by 6

Systematic literature review of the techniques for household electrical appliance anomaly detections and knowledge extractions

Rauf, S. A. A., & Adekoya, A. F. (2023) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-023-00086-1 · Cited by 6

Anomaly Detection for Space Information Networks: A Survey of Challenges, Schemes, and RecommendationsPreprint

Diro, A. A., Kaisar, S., Vasilakos, A. V., Anwar, A., Nasirian, A., & Ganfure, G. O. (2023) · doi:10.36227/techrxiv.23584530 · Cited by 6

Systematic review and characterisation of malicious industrial network traffic datasets

Dobler, M., Hellwig, M., Lopes, N., Oakley, K., & Winterburn, M. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01126-9 · Cited by 5

Defending against smishing attacks: State-of-the-art techniques, challenges, limitations, and future directions

Rahaman, M., Cajes, N. C., Gupta, B. B., Chui, K. T., & Nedjah, N. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111758 · Cited by 5

Unsupervised detection of faults in industrial pumps from multivariate time series

Shaikh, F., Ahmed, B. S., & Swerin, A. (2025) · Machine Learning with Applications · doi:10.1016/j.mlwa.2025.100784 · Cited by 7

Process mining-driven modeling and simulation to enhance fault diagnosis in cyber–physical systems

Vitale, F., Dall’Ora, N., Gaiardelli, S., Fraccaroli, E., Mazzocca, N., & Fummi, F. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.12.005 · Cited by 5

Reliable and Secure Anomaly Detection in Heterogeneous Federated Learning: A Comprehensive Review

Xiang, H., Wang, G., Yu, X., Di, F., Gao, R., Zhang, Y., & Han, X. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020067 · Cited by 7

Secure IoT sensor networks through advanced anomaly detection with Kolmogorov–Arnold Networks (KANs)

Mishra, S., & Jain, U. (2025) · Microsystem Technologies · doi:10.1007/s00542-025-05848-7 · Cited by 5

Analysis of recent trends and developments in IoT-based onion storage monitoring systems: a systematic review

Wakchaure, V., & Pawase, R. S. (2025) · International Journal on Smart Sensing and Intelligent Systems · doi:10.2478/ijssis-2025-0013 · Cited by 5

Network traffic anomaly detection method based on stacked fusion time features

Hou, T., Zhang, Z., Wu, Q., Yan, Y., & Li, Z. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111729 · Cited by 5

Anomaly Detection - Recent Advances, AI and ML Perspectives and Applications

Krishna Parimala, V. (2023) · Artificial intelligence · doi:10.5772/intechopen.110988 · Cited by 5

Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City

Hasani, Z., Krrabaj, S., & Krasniqi, M. (2024) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v18i03.44467 · Cited by 5

DWSP-MT: Discovery of workers sensing preferences to match tasks for improving data collection quality in MCS

Kang, Y., Liu, A., Zhang, S., Wang, T., Dong, M., & Song, H. H. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101198 · Cited by 5

An IIoT Temporal Data Anomaly Detection Method Combining Transformer and Adversarial Training

Tian, Y., Wang, W., & He, J. (2024) · International Journal of Information Security and Privacy · doi:10.4018/ijisp.343306 · Cited by 5

The Promise of Applying Machine Learning Techniques to Network Function Virtualization

Jmila, H., Khedher, M. I., & El‐Yacoubi, M. A. (2024) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi.2024.100020 · Cited by 6

IoT-CDS: Internet of Things Cyberattack Detecting System Based on Deep Learning Models

Abdullah, M. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.059271 · Cited by 7

Detection and Response Strategies for Advanced Persistent Threats (APTs)

Gilbert, C., Gilbert, M. A., & Dorgbefu, M. (2025) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v4i4.367 · Cited by 5

Anomaly Detection in Network Traffic for Proactive Security Threat Identification Using Improved Gated Recurrent Unit

Yarram, S., Musnmusawi, M., Deepika, J., Nagarathna, P., & Anandan (2025) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs65178.2025.10968381 · Cited by 4

A Comprehensive Survey of Threat Detection and Mitigation in Layered IoT Security Frameworks

Kavitha, L., & Shaik, K. (2025) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa66339.2025.11170923 · Cited by 4

Quantum-resistant blockchain and federated learning integrated secure and scalable smart grid framework for sustainable and privacy-preserving energy management

Yarrabothu, R. S., Benni, N. S., Nirmalraj, S., Mahesh Kumar, N., Singh, B. K., Divya, A., Bhojan, R., & Tirth, V. (2026) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2026.101305 · Cited by 4

Resilient Distributed Parameter Estimation for Sensor Networks Against Sparse-Varying Attacks

Lei, X., Wen, G., & Chen, G. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3451336 · Cited by 4

RT-Cabi: an Internet of Things based framework for anomaly behavior detection with data correction through edge collaboration and dynamic feature fusion

Li, X., & Chen, M. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2306 · Cited by 4

Anomaly Detection Based on Frequent Pattern Mining in Smart Home Devices

Ngo, M. H., Do, H., Pham, H.-D., do, Q.-H., & Nguyen, H.-S. (2025) · IEEE Access · doi:10.1109/access.2025.3603558 · Cited by 4

Time series anomaly detection methods for IoT in smart grids: A systematic literature review

Deng, T., Li, H., Cui, N., & Chen, N. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131967 · Cited by 4

Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model

Shi, T., Zou, Z., & Ai, J. (2023) · Applied Sciences · doi:10.3390/app132011349 · Cited by 4

Anomaly detection using deep learning approach for IoT smart city applications

Shibu, S., Kirubakaran, S., Remamany, K. P., Ahamed, S., Chitra, L., Kshirsagar, P. R., & Tirth, V. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19176-x · Cited by 4

Unveiling anomalies: transformative insights from transformer-based autoencoder models

Dehkordi, S. B., Nasri, S., & Dami, S. (2024) · International Journal of Computers and Applications · doi:10.1080/1206212x.2024.2441147 · Cited by 4

A Hybrid Learning Approach for Automatic Data Labelling and Anomaly Detection in IoT Networks

Kanwal, R., Kanwal, R., Noor, U., & Rashid, Z. (2023) · doi:10.1109/icai58407.2023.10136687 · Cited by 4

Dynamic Digital Twins: Challenges, Perspectives and Practical Implementation from a City’s Perspective

Richter, R., Knospe, F., Trapp, M., & Döllner, J. (2024) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-031-43699-4_41 · Cited by 4

SHAP enhanced transformer GWO boosting model for transparent and robust anomaly detection in IIoT environments

Aly, M., & Alotaibi, N. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-25033-0 · Cited by 3

Using mobile phone data for quantifying large-scale household-level disaster recovery

Swanson, T., & Guikema, S. D. (2025) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2025.102395 · Cited by 3

Anomal-EFD: A self-supervised model for anomaly detection in dynamic IoT networks

Fan, G., Huang, Q., Ma, J., Sanaullah, B. H. M., Zhang, C., & Yuan, K. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02184-5 · Cited by 3

Enhancing software-defined network security with deep learning: a comprehensive review

Shyryn, B., Ahanger, T. A., & Zhumadillayeva, A. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01232-2 · Cited by 5

Developing an Integrated Runtime Verification for Safety and Security of Industrial Robot Inspection System

Değirmenci, E., Kırca, Y. S., Örnek, Ö., Bulut, M., Kahraman, S., Özkan, M., & Yazıcı, A. T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43360-3_11 · Cited by 3

Privacy Preservation in Federated Learning: its Attacks and Defenses

Maurya, J., & Prakash, S. (2023) · International Conference on Pervasive Computing and Social Networking (ICPCSN) · doi:10.1109/icpcsn58827.2023.00177 · Cited by 3

A Self-Adapting IoT Network Configuration Supported by Distributed Graph Transformations

Jaskierny, L., & Kotulski, L. (2023) · Applied Sciences · doi:10.3390/app132312718 · Cited by 3

A self-learning algorithm for identifying the leverage points in soil data using quantile regression forests

Rose, S., Nickolas, S., Sunoj, S. M., & Sangeetha, S. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100375 · Cited by 3

Cyber Resilience in OT: Characteristics and Security Challenges

Cotiga, M., Pedersen, J. M., & Dushku, E. (2024) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr61664.2024.10679463 · Cited by 3

LogTraceAD: Anomaly Detection from Both Logs and Traces with Graph Representation Learning

Xie, X., Jian, S., & Huang, C. (2023) · International Conference on Networks, Communications and Information Technology (CNCIT) · doi:10.1145/3605801.3605824 · Cited by 3

A deep learning approach combining autoencoder with supervised classifiers for IoT anomaly detection

Noi, N. H., Hoa, D. V., & Ngọc, T. N. (2023) · Journal of Military Science and Technology · doi:10.54939/1859-1043.j.mst.csce7.2023.98-110 · Cited by 3

Stock market prediction under a deep learning approach using Variational Autoencoder, and kernel extreme learning machine

Hemant, H., Parida, A. K., Kumari, R., Singh, A. R., Bandyopadhyay, A., & Swain, S. (2023) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit59427.2023.10430718 · Cited by 3

Detection of Unfocused EEG Epochs by the Application of Machine Learning Algorithm

Akhter, R., & Beyette, F. R. (2024) · Sensors · doi:10.3390/s24154829 · Cited by 2

Advancing reliability and safety in iot: engineering cyber-physical human systems for the future

Ahmad, S., Ansari, M. A., Mewada, A., & Arif, M. (2025) · Life Cycle Reliability and Safety Engineering · doi:10.1007/s41872-025-00323-8 · Cited by 2

A Survey on Survivable Safety-Critical Systems

Silva, H. W. D., Vieira, M. P. A., & Neto, A. (2025) · IEEE Access · doi:10.1109/access.2025.3590244 · Cited by 2

IoE-GraphFormer: A Graph Transformer-Based Framework for Anomaly Detection in Internet of Everything

Corradini, E., Chen, W., & Cauteruccio, F. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00152 · Cited by 4

Iforestexplain: a dual-stage isolation forest framework with explainable AI for DoS/DDoS anomaly detection in IoT networks

Obidiagha, C. C., Rahouti, M., Drid, H., Hamouid, K., Medjadba, Y., & Jagatheesaperumal, S. K. (2025) · Cluster Computing · doi:10.1007/s10586-025-05365-2 · Cited by 2

An Automated Domain-Agnostic and Explainable Data Quality Assurance Framework for Energy Analytics and Beyond

Tolnai, B. A., Ma, Z., Jôrgensen, B. N., & Ma, Z. G. (2025) · Information · doi:10.3390/info16100836 · Cited by 2

Process Anomaly Detection in Cyber–Physical Production Systems Based on Conditional Discrete-Time Dynamic Graphs

Goetz, C., & Humm, B. G. (2025) · Applied Sciences · doi:10.3390/app152111354 · Cited by 2

A Systematic Review of Concept Drift Approaches for Anomaly Detection in IoT Networks

Abdulrahaman, M. D., Balogun, N. A., & Oloyede, M. O. (2025) · SN Computer Science · doi:10.1007/s42979-025-04624-8 · Cited by 2

A hybrid ensemble framework for unknown attack detection in IoT networks

Wang, C.-H. J., Kang, H., Lam, U., Seo, J. T., & Kim, D. S. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108447 · Cited by 2

Machine Learning Approach for Anomaly Detection in Smart Home Network Traffic

Ismagulov, Y., & Adamova, A. (2026) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec69317.2026.11418439 · Cited by 2

Recent trends in artificial intelligence for anomaly detection and their applications in low power wireless networks

Kim, J., Suh, B., Park, Y. C., Kim, K., Ullah, S., & Bhargava, A. (2026) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2026.102214 · Cited by 2

TLS-Aware Anomaly Detection for Encrypted IoT Traffic Using a β-Variational Autoencoder with ANOVA–Mutual Information Feature Selection

Nouman, M., Ujjan, R. M. A., & Saleh, M. H. (2026) · Future Internet · doi:10.3390/fi18060310 · Cited by 2

PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution

Xiao, Y., Zhu, J., Yan, S., Song, H. T., & Zhang, S. (2023) · Applied Sciences · doi:10.3390/app13137458 · Cited by 2

An Adaptive Detection Mechanism for IoT Devices Anomalies Using AI/ML Based on User Pattern

Rajendra, S. A., Pradhan, C., & Kanniappan, J. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9043-6_2 · Cited by 2

EPOPTIS: A Monitoring-as-a-Service Platform for Internet-of-Things Applications

Zervoudakis, P., Karamolegkos, N., Plevridi, E., Charalampidis, P., & Fragkiadakis, A. G. (2024) · Sensors · doi:10.3390/s24072208 · Cited by 2

Survey and Analysis of Anomaly Detection for Smart Home using Microcontroller

Rami, R., & Malek, Z. S. (2024) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi61536.2024.00130 · Cited by 2

An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review

Alsoufi, M. A., Siraj, M. M., Ghaleb, F. A. A., Abdulqader, A. H., Ali, E., & Omar, M. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-59707-7_20 · Cited by 2

Advancing Network Survivability and Reliability: Integrating XAI-Enhanced Autoencoders and LDA for Effective Detection of Unknown Attacks

Stodt, F., Théoleyre, F., & Reich, C. (2024) · International Conference on the Design of Reliable Communication Networks (DRCN) · doi:10.1109/drcn60692.2024.10539141 · Cited by 2

End-to-End Framework for Identifying Vulnerabilities of Operational Technology Protocols and Their Implementations in Industrial IoT

Boeding, M., Hempel, M., & Sharif, H. (2025) · Future Internet · doi:10.3390/fi17010034 · Cited by 2

Reviewing progresses on In-Sensor AI Computing

Pau, D. P., & Tognocchi, S. (2025) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2025.105156 · Cited by 2

AI-Driven Detection and Mitigation of Backdoor Data Infiltration Among IoT Networks

Pasha, S. M. M., Ye, C., Sohag, S. R., Ali, M. M., & Islam, M. T. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229034 · Cited by 2

Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression

Stropeni, A., Borsatti, F., Barusco, M., Pezze, D. D., Fabris, M., & Susto, G. A. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.12.073 · Cited by 2

Chain-Based Outlier Detection for Complex Data Scenarios

Dong, H., Wang, Q., & Ding, W. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386264 · Cited by 1

Boundary-Driven Active Learning for Anomaly Detection in Time Series Data Streams

Zhou, X., Wang, Y., Xu, H., & Liu, M.-Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446524 · Cited by 1

Framework for Real-Time Predictive Maintenance Supported by Big Data Technologies

Teixeira, M., Thierstein, F., Entringer, P., Sá, H., Leitão, J. D., & Leal, F. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-60215-3_2 · Cited by 1

Analysis of Intelligent Water Management: Integration of IoT and AI in Water and Wastewater Treatment

Movva, S. S. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24322073844 · Cited by 1

Emerging application use cases and future directions

Ghorpade, S. N., & Chougule, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00024-5 · Cited by 1

Enhancing Predictive Maintenance in Industrial IoT: A Machine Learning Approach for Real-time Anomaly Detection in Pressure Switches

Soyhan, B., & Can, Z. (2025) · Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi · doi:10.53608/estudambilisim.1574345 · Cited by 1

Comparative Evaluation of Autoencoders for Semi-Supervised Anomaly Detection on Univariate Time Series Data

Markovic, T., Moricz, S., & León, M. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00206 · Cited by 1

ADIoT: An Anomaly Detection Model for IoT Devices Based on Behavioral Feature Analysis

Wang, L., Wang, Z., & Wang, M. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00273 · Cited by 1

Improving IoT Intrusion Detection Using GAN-Based Synthetic Traffic Augmentation

Hojjatinia, S., Monshizadeh, M., Khatri, V., Mähönen, P., & Yan, Z. (2025) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft64993.2025.11080628 · Cited by 1

Lightweight Intrusion Detection System for MQTT-Enabled IoT Devices

Udurume, M., Shakhov, V., & Koo, I. (2025) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn65838.2025.11170063 · Cited by 1

ON-NSW: Accelerating High-Dimensional Vector Search on Edge Devices with GPU-Optimized NSW

Park, T., Lee, H., Na, Y., & Kim, W.-H. (2025) · Sensors · doi:10.3390/s25206461 · Cited by 2

An Energy-Efficient Framework for Real-Time Anomaly Detection and Threat Mitigation in IoT Traffic Streams

Berraadi, O., Tani, H. G., & Ahmed, M. B. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-032-04114-2_6 · Cited by 1

A feedback-driven anomaly detection framework for smart homes: an anomaly-enhanced activity recognition iteration (AEARI) approach

Aziz, K., Dua, S., & Gupta, P. (2025) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-025-05015-w · Cited by 1

Anomaly-Aware Graph-Based Semi-Supervised Deep Support Vector Data Description for Anomaly Detection

Alhindi, T. J. (2025) · Mathematics · doi:10.3390/math13243987 · Cited by 2

TinyML for Acoustic Anomaly Detection in IoT Sensor Networks

Almaini, A. E. A., Folz, J., & Ashour, G. (2025) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme64568.2025.11277514 · Cited by 1

Self-Supervised Intelligent Autoencoder with Multi-Scale Convolutional Contrastive Learning for Multivariate Anomaly Detection

Lu, J., Fang, Y., Fan, S., & Wu, X. (2025) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967251407728 · Cited by 1

Machine Learning-Driven Anomaly Detection in Aviation IoT Systems

Rumani, D. D., Nasution, D., & Sulistianingsih, I. (2025) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic68054.2025.11309426 · Cited by 1

Quantum Machine Learning for Anomaly Detection in IoT Devices

Chowdhury, S. A., Karim, S. F., Bhuiyan, M. S. I., Shuvro, A. A., & Hossen, M. S. (2025) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1109/aimlsystems67835.2025.11331191 · Cited by 1

Embedding Federated Learning in IoT Governance: An Extension to the TpM+ Methodology

Yamaguti, R., de Paula, F. G., Ferreira, B. C. L. C., Branquinho, O. C., & Cardieri, P. (2025) · Symposium on Internet of Things (SIoT) · doi:10.1109/siot68426.2025.11368810 · Cited by 1

A Robust GNN-based Framework for IoT Anomaly Detection with Adaptive Attention and Adversarial Robustness

Zhu, L., & Zhang, X. (2025) · International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) · doi:10.1109/ccpqt66408.2025.11383316 · Cited by 1

Proximity-based explainable anomaly detection for time-series data with calibration, leakage safety

İnan, E. (2026) · Internet of Things · doi:10.1016/j.iot.2026.101920 · Cited by 1

Applications of anomaly detection algorithms in identifying abnormal events

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