IoT anomaly detection methods and applications: A survey

Anomaly detection: A survey

Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687

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

Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240

A Review on Outlier/Anomaly Detection in Time Series Data

Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2022) · ACM Computing Surveys · doi:10.1145/3444690 · Cited by 1,102

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

Online detection of analog signal anomaly based on the evaluation of local trends

Liu, Y., Jiang, B., & Yi, H. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad02b3 · Cited by 1

Power Consumption Analysis as a Detection Indicator for Cyberattacks on Smart Home Devices

Schorr, V., Kamenev, N., Bleistein, T., Werth, D., Wendzel, S., & Weigold, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48652-4_15 · Cited by 1

Local Outlier Detection Based on K-distance Variation to Enhancing Imaging-aided Diagnosis

Dong, H., Li, R., Wang, W., Wu, S., Xu, G., Zhao, Z., & Wang, Q. (2023) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc59560.2023.10386587 · Cited by 1

Feedforward Mutual-Information Anomaly Detection: Application to Autonomous Vehicles

McKee, S. M., Haddadin, O. S., & Leang, K. K. (2022) · Journal of Autonomous Vehicles and Systems · doi:10.1115/1.4064519 · Cited by 1

Review of Abnormalities Detection in IoT Using Evolutionary Deep Learning

Kokaz, S. R., Albu-Salih, A. T., & Ghandour, A. (2023) · International Conference on Information Technology, Applied Mathematics and Statistics (ICITAMS) · doi:10.1109/icitams57610.2023.10525331 · Cited by 1

Automated Anomaly Detection in Concentrated Solar Power Plants with Linear Least Squares Approximation

Jungnickel, R., Olbrych, S., Zeng, M., Kraft, T., Behrens, F., Gannouni, A., Abdelrazeq, A., & Schmitt, R. H. (2024) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit58233.2024.10540932 · Cited by 1

Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis

Rath, S., Upadhyay, H., Prakash, S., & Raja, H. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-1841-2_6 · Cited by 1

A review of data analytic algorithms for outlier detection on the internet of things ecosystem

Iwomi, O. J., Abel, E. E., Omede, G. C., Atonuje, E. O., Ogeh, C. O., Akazue, M. I., & Apanapudor, J. S. (2024) · Science World Journal · doi:10.4314/swj.v19i2.26 · Cited by 1

Anomaly Detection from Time Series Under Uncertainty

Wiessner, P., Bezirganyan, G., Sellami, S., Chbeir, R., & Bungartz, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-68323-7_18 · Cited by 1

Development of a Personalized Anomaly Detection Model to Detect Motion Artifacts Over PPG Data Using Catch22 Features

Valerio, A., Demarchi, D., O’Flynn, B., & Tedesco, S. A. (2024) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors60989.2024.10785142 · Cited by 1

An Empirical Examination of Machine Learning-Based Attack Classification in Internet of Things Networks

Verma, V. K., Guhan, K., Ramakrishna, C., Verma, S., Sharma, A. K., & Praveena, S. (2024) · IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS) · doi:10.1109/icetas62372.2024.11120205 · Cited by 1

Aberrant Behavior Monitoring in IoT Environments Using Deep Learning

Biswas, A. P., Singh, K., Yukta, Y., & Singh, D. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6300-2_14 · Cited by 1

Detecting and reacting to smart home novelties

Holder, L. B., Eaves, B., Shafto, P., Pereyda, C., Thomas, B., & Cook, D. J. (2025) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-025-01158-8 · Cited by 1

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.v1 · Cited by 1

IoT-enabled B2B IntegrationPreprint

Usman, N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4746117 · Cited by 1

Outlier Detection in IoT using Trained Autoencoder and Contrastive LossPreprint

Nikougoftar, E. (2024) · Research Square · doi:10.21203/rs.3.rs-4657376/v1 · Cited by 1

ARQUITETURA DE DETECÇÃO DE INTRUSÃO POR ANOMALIAS COM FEDERATED LEARNING EM REDES IoT

Soares, S. C. M., Filho, F. L. D. C., Soares, M. F., de Mendonça, F. L. L., & de Sousa, E. D. C. E. R. T., Jr. (2022) · Conferência Ibero-Americana WWW/Internet (CIAWI) · doi:10.33965/ciaca_ciawi2022_202209l004

Multivariate Time-series Data Correction by combining Attention-based LSTM and GAN Model

Jeong, H., Jeong, J., Chun, J., & Kim, H. (2023) · International Conference on Applied System Innovation · doi:10.1109/icasi57738.2023.10179548

RTAD-TP: Real-Time Anomaly Detection Algorithm for Univariate Time Series Data Based on Two-Parameter Estimation

Fan, Q., Tang, Y., Ding, X., Huangfu, Q., & Ding, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40283-8_9

Implementation and analysis of recombination techniques within the receiver for IoT based applications

Elaage, S., Ghzaoui, M. E., Mrani, N., & Alami, R. E. (2023) · Wireless Networks · doi:10.1007/s11276-023-03501-7

Enhancing Maritime Safety Through IoT-Integrated High-Pressure Water Mist Fire Fighting System

Babu, K. V., Gowtham, M., Soorya, A., & Joel Praveen., A. (2023) · International Conference on Pervasive Computing and Social Networking (ICPCSN) · doi:10.1109/icpcsn58827.2023.00239

Container Security Using Algorithmic Approach

Ramanayaka, R. P. N. M., & Abeywickrama, J. A. S. T. (2023) · International Research Journal of Innovations in Engineering and Technology · doi:10.47001/irjiet/2023.711066

Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique in IoT Healthcare Data

Subha, S., & Sathiaseelan, J. G. R. (2024) · Indian Journal of Science and Technology · doi:10.17485/ijst/v17i5.1645

АНАЛІЗ МЕТОДІВ ВИЯВЛЕННЯ АНОМАЛЬНОГО ТРАФІКУ В МЕРЕЖАХ IOT

Marchenko, R., Kovalenko, A., & Znaidiuk, V. (2024) · Системи управління навігації та зв’язку Збірник наукових праць · doi:10.26906/sunz.2024.1.133

EXPLORING IOT ARCHITECTURE: A COMPARATIVE ANALYSIS OF TECHNOLOGIES AND ENHANCING SECURITY IN DEVICE FIRMWARE

(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets50177

Internet of Things Security Using Blockchain

Shamala, L. M., Balasaraswathi, V. R., & Sharma, A. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-1511-8.ch002

Impact of Machine Learning in Cybersecurity: A Critical Review with Insights on the Internet of Things

Utsav, O., Saini, G. L., & Panwar, D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9531-8_29

Mass-Ratio-Average-Absolute-Deviation Based Outlier Factor for Anomaly Scoring

Fan, Z., Luangsodsai, A., & Sinapiromsaran, K. (2024) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse61278.2024.10613697

ENERGY EFFICIENT SECURITY PROTOCOL FOR HEALTHCARE INTERNET OF THINGS

Babu, R. M., Malluvalasa, S. N. L., Devi, M. K., Karthik, S. S., Rakesh, M., & Prasad, S. K. (2024) · Telecommunications and Radio Engineering · doi:10.1615/telecomradeng.2024052056

Security of the IoT Ecosystem: Current and Future Directions

Shukla, T., & Mishra, N. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2451-2_15

Novel Hybrid One-Class RNN Model Using Adversarial Data Augmentation for Anomaly Detection in Systems Operations

Jaisharma, K., Devi., T., & Deepa, N. (2024) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs60874.2024.10717289

Real-Time Anomaly Detection in IoT Networks Using Deep Neural Networks

Isaac, L. D., Mohanraj, V., Sathiyamoorthi, V., Prasuna, K., & Kalipindi, M. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-4252-7.ch001

A Data-Driven Framework for Identifying Abnormal Status in Natural Gas Wells

Cao, Y., Ma, Y., Tang, X., Rasheed, R. A., & Li, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0811-9_8

Explainable Process Deviance Discovery with Data-Efficient Deep Learning

Folino, F., Gianluigi Folino, A., Guarascio, M., & Pontieri, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-81241-5_6

Security and privacy of IoT applications

Abusitta, A. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21640-4.00008-9

Surprisal-based algorithm for detecting anomalies in categorical data

Cherkaoui, O., Anoun, H., & Maizate, A. (2025) · Data Science and Management · doi:10.1016/j.dsm.2025.01.005

Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Li, Y., Wang, Y., Xu, H., & Zhou, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889109

Enhancing IoT Security: A Comparative Analysis of Machine Learning Algorithms for Attack Detection

Garg, M., & Síma (2025) · International Conference on Pervasive Computational Technologies (ICPCT) · doi:10.1109/icpct64145.2025.10940390

Fraud Detection in IoT Networks

Bhagya, B., Thampi, S. M., & Mukherjee, P. (2025) · Springer eBooks · doi:10.1007/978-3-031-82826-3_5

Unsupervised Dual Convolutional Autoencoder Models for Efficient Group Anomaly Detection of HST Bogies via Domain Adversarial Learning

Chang, Y., Shuncong Zhong, A., Pan, T., & Xie, J. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2999/1/012029

Invisible Shield: Real-Time Industrial Threats Detection for IoT-based Systems

Sudhakar, K., Manish, P., Rohit, M., Ahmed, S., & Sneha, B. (2025) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs65191.2025.10984685

Deep Learning-Driven SIoT Security: Detecting Anomalies and Community Attacks with SPY IoT Devices

Anciline Jenifer, J., & K., P. P. S. (2025) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict64420.2025.11005346

The Best of Both Worlds: On Repairing Timestamps and Attribute Values for Multivariate Time Series

Zhu, J., Deng, W., Sun, Y., Song, S., Zhang, H., & Yuan, X. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725367

Comparative Analysis of Detecting Anomalies in Real-Time Streaming Data

Belgaum, M. R., Suresh, K., Tharun, M., Sreenivasulu, N., & Satyanarayana, C. V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2179-8_37

In Specs We Trust? Conformance-Analysis of Implementation to Specifications in Node-RED and Associated Security Risks

Schneider, S., Kashish, K., Tuma, K., & Scandariato, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00624-0_14

Network Anomaly Detection Using Gramian Angular Field Transformation and Vision Transformer

Kobiela, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02049-9_16

Accurate and Scalable Anomaly Detection in Maternal Health Monitoring Systems

Sang, Y., Fang, W., Xu, Y., & Zhang, X. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010015

MRMamba: Multi-Resolution State-Space Modeling for Robust Multisensor Traffic Attack Detection in Wireless Sensor Networks

Liu, S., He, Q., Chen, Y.-T., Zhang, F., & Liu, A. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3616339

Reconstruction-Based Anomaly Detection in Vibration Data with Deep State Space Models

Kim, K. S., Gwak, M., Ryu, S. H., & Park, P. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11178820

An Anomaly Detection Model for Streaming Data with Integrated Quality of Service Optimization

Gao, C., Liu, J., Wang, Q., Pei, Q., & Chen, Y. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11179756

Next-Generation Networks: An IoT Perspective on Anomaly and Intrusion Detection

sunitha, K., & Shaik, K. (2025) · International Conference on Inventive Systems and Control (ICISC) · doi:10.1109/icisc65841.2025.11187577

IoT Security Landscape: A Review of Threat Detection, Trust Management and Resilient Communication

Nara, N., & Jyothsna, V. (2025) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia67127.2025.11200959

Improving IIoT Anomaly Detection Precision via XAI-Guided Feature Engineering and LSTM Tuning on Imbalanced Data Under Resource Constraints

Ahmad, W. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-07373-0_49

Anomaly Detection With Machine Learning

Shavali, T. K. S., Mahale, S. V., Manikandan, S., Chaudhari, S. V., Kunwar, F. B., Sandeep, C. S., & Bhoopathy, V. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2252-0.ch004

Faro: A Scalable and Reliable Outage Detection Algorithm for IoT Mobile Virtual Network Aggregators

Milani, M., Vomhoff, V., Bega, D., Gramaglia, M., Geißler, S., Karsai, C., & Serrano, P. (2025) · Proceedings of the ACM on Networking · doi:10.1145/3768982

Clustering Algorithms for Anomaly Detection in EVCS Infrastructure using OCPP

Tchassem, C. T., Kombate, Y., & Tardif, P.-M. (2025) · Conference on Privacy, Security and Trust · doi:10.1109/pst65910.2025.11268878

AdaptiveMECShield: A trust-based, threshold-aware framework for real-time DDoS mitigation in mobile edge computing

Ramaleba, J., & Velempini, M. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100826

Machine learning approaches for strengthening IoT security: A thorough review

Vasudevan, V., Gounder, M. S., & Mohan, U. S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34125-0.00029-5

TE-LSTM-AE: Leveraging Latent Space Trends for Slow and Fast Anomaly Detection in Industrial Control Systems

Abdullahi, B., & Vadera, S. (2025) · International Conference on Computer Vision and Data Mining (ICCVDM) · doi:10.1109/iccvdm66874.2025.11290063

A Real-Time Deep Neural Network-Based System’s Ability to Identify and Classify Active Assaults on Networks

Kaliyaperumal, K., Boddu, R. S. K., & Oruganti, S. K. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.11.1540-1559

Deep Learning for Collective Anomaly Detection

Khettaf, D., Djenouri, D., Rezaeifar, Z., & Djenouri, Y. (2026) · ACM Computing Surveys · doi:10.1145/3788280 · Cited by 1

A Sustainable Framework for Intelligent Disaster Forecasting Through IoT-Based Environmental Sensing

Damle, M., & Das, I. (2025) · International Conference on Digital Innovations for Sustainable Solutions (ICDISS) · doi:10.1109/icdiss68238.2025.11320601

Leveraging Symbolic Encoding for Performance Anomaly Classification in Dynamic IoT Pipelines

Patel, M., Mukhopadhyay, S., & Mukherjee, J. (2025) · IEEE International Conference on Collaborative Advances in Software and Computing (CASCON) · doi:10.1109/cascon66301.2025.00062

Anomalies and Cyberattacks Detection Model in Resource Constrained IoT Networks

Mohanta, H., Bhatia, J. S., Dasgupta, J., Shekar, K., Kadhim, A. M., & Fatoohi, S. K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14189-7_14

A Survey on Extended Reality in Disaster Management: Integrating XR, IoT, and Machine Learning for Enhanced Situational Awareness

Krasniqi, E., & Shehu, V. (2025) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine66377.2025.11340049

Instantaneous Detection of Anomalous Behavior in IoT Systems via Responsive Learning Algorithms

Kumar, N. P., & Reddy, B. R. S. (2025) · IEEE International Conference on Advanced Computing Technologies (ICACT) · doi:10.1109/icact67549.2025.11351500

A DEPMU-based network traffic anomaly detection scheme for IoT

Liu, Y., Li, C., Yang, C., & Ding, Y. (2026) · Ad Hoc Networks · doi:10.1016/j.adhoc.2026.104160

Next-Gen Contextual Anomaly Detection for Sustainable and Efficient Production in Industry 4.0

Fathi, K., Sadurski, M., Waskow, S., Kleinert, T., & van de Venn, H. W. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15576-4_13

Enhancing Anomaly Detection in Wireless Sensor Networks: A Review of Machine Learning and Neural Network-Based Approaches

Babu, A., & Bagubali, A. (2026) · IEEE Access · doi:10.1109/access.2026.3663718

Anomaly Detection for IoT Global Connectivity

Iglesias, J. O., Perales, C. S., Geißler, S. M., Perino, D., & Lutu, A. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3666123

R+R: IoT Device Identification Under Realistic Conditions

Gupta, C., Peter, A., & Continella, A. (2025) · Annual Computer Security Applications Conference · doi:10.1109/acsac67867.2025.00071

Using Trend Analysis for Supporting Environment Monitoring Through IoT-based Systems

da Silva, W. F., de Souza, V. P., Gamileira, F. G. S., Santos, E. M. D., Chiavegatto, R., Santos, G. E., Brito, A. C., & Rodrigues, E. (2025) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms68530.2025.11408618

Anomaly Detection in IoT Environments Using Machine Learning: A Bibliometric Review, Challenges, and Future Research Directions

Siddiqui, M. A., Kalra, M., & Krishna, C. R. (2026) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70605

A Real-Time Remote Sensing Network Situation Awareness Architecture Based on Generative Adversarial Networks

Xia, Y., & Qi, W. (2025) · International Conference on the Frontiers of Electronic, Electrical and Information Engineering (ICFEEIE) · doi:10.1109/icfeeie66944.2025.00052

TextBACON: An Unsupervised, Statistically Robust Approach To Textual Outlier Detection via Embeddings

Gallab, J. E., Bolock, A. E., & Hadi, A. S. (2026) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami68106.2026.11420826

Identification of Abnormal UGW Signals Using Multi-Scale Progressive Reconstruction Network

Zou, Y., Wu, J., Ye, B., Honggui, C. A. O., Yang, C., & Cui, Y. (2026) · Acoustics · doi:10.3390/acoustics8010020

AI-Enhanced IoT Mechatronic Platform for Assisted Mobility and Safety Monitoring in Small Dogs Based on Laser-Induced Graphene Contact Temperature Sensing

Sánchez, A. C., Pantoja-Suárez, F., & Segovia, D. (2026) · Applied Sciences · doi:10.3390/app16063100

Design and Evaluation of a Lightweight Cybersecurity Architecture for 5G-Connected IoT in Smart Transportation

Sousa, A., Abreu, R., Correia, L., & Reis, M. J. C. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-05748-8_31

Knowledge-based Systems for Cybersecurity in Internet of Things Environments

Khan, A. Q. (2024) · theses.fr (ABES) · doi:10.70675/9ce779a7z982dz436cza13fzba5c552319ed

Hardware aware federated learning with sparse models for adaptive anomaly detection in IoT

Iqbal, R., Ismail, S. S., & Rathee, G. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01171-w

Multi-objective optimization-based sequential clustering and classification approach for anomaly detection and root cause analysis in the multi-stage manufacturing process

Nguyen, T. P. Q., Hoang, T. N. H. G., & Le, T. H. A. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae6421

RADAR: Robust anomaly detection and recognition. A unified and reproducible framework across tabular, time-series, and federated data

García, B. B., Aguilera-Martos, I., Rodríguez-Barroso, N., Herrera, F., & Luengo, J. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133775

A Trade-Off Aware Analysis of Lightweight Neural Networks for IoT Sensor Data Processing

Goud, A., Loonkar, S., & Desai, K. (2026) · Contemporary Computing Innovations Conference (CCIC) · doi:10.1109/ccic68129.2026.11486132

Federated Learning-Based Secure Access Control and Anomaly Detection in IoMT Systems

Manoj, N. V. S., Aswathi, M., Nandana, N., & Sankaran, S. (2025) · IEEE International Symposium on Smart Electronic Systems (iSES) · doi:10.1109/ises67504.2025.00057

Robust unsupervised outlier detection in IoT using contrastive learning-driven autoencoders

Gu, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-51397-y

Faults Detection in Wireless Sensor Networks (WSN) Using One Dimensional Convolution Neural Network CNN-1D

Gran, F. M., Bouazzi, K., Saadaoui, W., & Helali, A. (2026) · IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) · doi:10.1109/ic_aset69920.2026.11502385

Adaptive Fuzzy-Threshold ConvLSTM Model for Efficient Real-Time WBAN Anomaly Identification

Pabitha, B., Vani, V., & Sanshi, S. (2026) · SN Computer Science · doi:10.1007/s42979-026-05016-2

Enhancing Internet of Things security through artificial intelligence and digital twins

Videsjorden, A. N., Göknil, A., Rauniyar, A., Husom, E. J., Dautov, R., Tudorache, L., & Kurtev, I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45573-5.00032-x

Lightweight U-Net Autoencoders with Semantic Compression for Unsupervised Fault Detection in Water Distribution Systems

Li, Q., Islam, M. B., Chen, S., Qi, W., Guarascio, M., Vinci, A., Guerrieri, A., Cicirelli, F., & Fortino, G. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536214

Mapping the recent landscape of anomaly detection beyond cybersecurity and finance: a Web of Science bibliometric and topic-modelling review

Albu, O., Chinie, A. C., & Berbece, D.-C. (2026) · Management & Marketing · doi:10.1007/s44491-026-00006-9

Comparative Analysis of Machine Learning Models for Anomaly Detection in IoT Networks

Ali, M. M., Alazemi, N. S., Almesaideen, S., & Hilal, S. (2025) · International Conference on Data Analytics for Business and Industry (ICDABI) · doi:10.1109/icdabi67967.2025.11547441

Uma estrutura inteligente baseada em IoT para monitoramento de energia em tempo real e otimização preditiva em ambientes da indústria 4.0

de Andrade, C. D. Q., do Valle, E. M., & Dias, E. M. S. (2026) · Cuadernos de Educación y Desarrollo · doi:10.55905/cuadv18n6-013

Real-time anomaly detection and blocking in IoT networks using NFQUEUE and priority queue with dashboard monitoring

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