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

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

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

Federated-Learning-Based Anomaly Detection for IoT Security Attacks

Mothukuri, V., Khare, P., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., & Srivastava, G. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3077803 · Cited by 787

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

Munir, M., Siddiqui, S. A., Dengel, A. R., & Ahmed, S. (2018) · IEEE Access · doi:10.1109/access.2018.2886457 · Cited by 752

DÏoT: A Federated Self-learning Anomaly Detection System for IoT

Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00080 · Cited by 663

Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Himeur, Y., Ghanem, K., Alsalemi, A., Bensaali, F., & Amira, A. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116601 · Cited by 660

Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT

Chen, Z., Chen, D., Zhang, X., Yuan, Z., & Cheng, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3100509 · Cited by 638

Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach

Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., & Hossain, M. S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3011726 · Cited by 620

ICC 2022 - IEEE International Conference on Communications

(2022) · doi:10.1109/icc45855.2022 · Cited by 610

Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning

Lu, H., Li, Y., Mu, S., Wang, D., Kim, H., & Serikawa, S. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2737479 · Cited by 505

Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Cities

Shen, M., Tang, X., Zhu, L., Du, X., & Guizani, M. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2901840 · Cited by 455

Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

Yin, C., Zhang, S., Wang, J., & Xiong, N. N. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.2968516 · Cited by 438

Variational LSTM Enhanced Anomaly Detection for Industrial Big Data

Zhou, X., Hu, Y., Liang, W., Ma, J., & Jin, Q. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3022432 · Cited by 403

Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions

Elazhary, H. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.10.021 · Cited by 328

Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence

Lv, Z., Han, Y., Singh, A. K., Manogaran, G., & Lv, H. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2994747 · Cited by 321

Machine learning approaches to IoT security: A systematic literature review

Ahmad, R., & Alsmadi, I. M. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100365 · Cited by 317

CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks

Ullah, W., Ullah, A., Haq, I. U., Muhammad, K., Sajjad, M., & Baik, S. W. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09406-3 · Cited by 311

Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-ArtPreprint

Braei, M., & Wagner, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00433 · Cited by 310

LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT

Wu, D., Jiang, Z., Xie, X., Wei, X., Yu, W., & Li, R. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2952917 · Cited by 298

Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective

Garg, S., Kaur, K., Kumar, N., & Rodrigues, J. J. P. C. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2893549 · Cited by 269

IoT Healthcare Analytics: The Importance of Anomaly Detection

Ukil, A., Bandyoapdhyay, S., Puri, C., & Pal, A. (2016) · International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2016.158 · Cited by 245

Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Wu, M., Song, Z., & Moon, Y. B. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-017-1315-5 · Cited by 238

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Fahim, M., & Sillitti, A. (2019) · IEEE Access · doi:10.1109/access.2019.2921912 · Cited by 238

Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection

Shorman, A. R., Faris, H., & Aljarah, I. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01387-y · Cited by 229

2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks

(2014) · doi:10.1109/dsn30607.2014 · Cited by 225

IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge

Hafeez, I., Antikainen, M., Ding, A. Y., & Tarkoma, S. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.2966951 · Cited by 201

Data Fusion Approach for Collaborative Anomaly Intrusion Detection in Blockchain-Based Systems

Liang, W., Xiao, L., Zhang, K., Tang, M., He, D., & Li, K. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3053842 · Cited by 198

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

(2018) · doi:10.1109/noms42834.2018 · Cited by 197

Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder

Chen, T., Liu, X., Xia, B., Wang, W., & Lai, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2977892 · Cited by 174

An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

Al-Haija, Q. S. A., McCurry, C., & Zein-Sabatto, S. (2020) · Electronics · doi:10.3390/electronics9122152 · Cited by 164

2015 IEEE 35th International Conference on Distributed Computing Systems

(2015) · doi:10.1109/icdcs35509.2015 · Cited by 161

A graph neural network method for distributed anomaly detection in IoT

Protogerou, A., Papadopoulos, S., Drosou, A., Tzovaras, D. K., & Refanidis, I. (2020) · Evolving Systems · doi:10.1007/s12530-020-09347-0 · Cited by 135

A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Li, H., & Boulanger, P. (2020) · Sensors · doi:10.3390/s20051461 · Cited by 130

2006 IEEE 63rd Vehicular Technology Conference

(2006) · doi:10.1109/vtcspring10498.2006 · Cited by 130

Real-time prediction and anomaly detection of electrical load in a residential community

Wang, X., & Ahn, S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114145 · Cited by 121

A framework for anomaly detection and classification in Multiple IoT scenarios

Cauteruccio, F., Cinelli, L., Corradini, E., Terracina, G., Ursino, D., Virgili, L., Savaglio, C., Liotta, A., & Fortino, G. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.08.010 · Cited by 120

GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in Internet of things

Aljawarneh, S. A., & Radhakrishna, V. (2018) · The Journal of Supercomputing · doi:10.1007/s11227-018-2397-3 · Cited by 113

LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things

Wang, J., Tang, Y., He, S., Zhao, C., Sharma, P. K., Alfarraj, O., & Tolba, A. K. R. (2020) · Sensors · doi:10.3390/s20092451 · Cited by 109

Multi-level trust-based intelligence schema for securing of internet of things (IoT) against security threats using cryptographic authentication

Mabodi, K., Yusefi, M., Zandiyan, S., Irankhah, L., & Fotohi, R. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-03137-5 · Cited by 107

GAN-Based Data Augmentation Strategy for Sensor Anomaly Detection in Industrial Robots

Lu, H., Du, M., Qian, K., He, X., & Wang, K. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3069452 · Cited by 98

Learning Latent Representation for IoT Anomaly Detection

Vu, L., Cao, V. L., Nguyen, Q. U., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3013416 · Cited by 95

Anomaly detection and trust authority in artificial intelligence and cloud computing

Qureshi, K. N., Jeon, G., & Piccialli, F. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107647 · Cited by 95

Deep Neural Network Security Collaborative Filtering Scheme for Service Recommendation in Intelligent Cyber–Physical Systems

Liang, W., Xie, S., Cai, J., Xu, J., Hu, Y., Xu, Y., & Meikang Qiu, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3086845 · Cited by 95

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said, A. M., Yahyaoui, A., & Abdellatif, T. (2021) · Sensors · doi:10.3390/s21041026 · Cited by 94

Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation

Zeng, X., Yang, M. Y., & Bo, Y. F. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106233 · Cited by 93

DeL-IoT: A deep ensemble learning approach to uncover anomalies in IoT

Tsogbaatar, E., Bhuyan, M. H., Taenaka, Y., Fall, D., Gonchigsumlaa, K., Elmroth, E., & Kadobayashi, Y. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100391 · Cited by 93

Novel approach for detection of IoT generated DDoS traffic

Cvitić, I., Peraković, D., Periša, M., & Botica, M. (2019) · Wireless Networks · doi:10.1007/s11276-019-02043-1 · Cited by 86

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Song, W., Beshley, M., Przystupa, К., Beshley, H., Кочан, О. В., Pryslupskyi, A., Pieniak, D., & Su, J. (2020) · Sensors · doi:10.3390/s20061637 · Cited by 86

Leveraging Semisupervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT Communication

Cheng, Y., Xu, Y., Zhong, H., & Liu, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3000771 · Cited by 84

The Blockchain Random Neural Network for cybersecure IoT and 5G infrastructure in Smart Cities

Serrano, W. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102909 · Cited by 84

Improved Long Short-Term Memory based anomaly detection with concept drift adaptive method for supporting IoT services

Xu, R., Cheng, Y., Liu, Z., Xie, Y., & Yang, Y. J. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.05.035 · Cited by 82

Residential building facade segmentation in the urban environment

Dai, M., Ward, W. O. C., Meyers, G., Tingley, D. D., & Mayfield, M. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107921 · Cited by 82

Short-Term PM2.5 Forecasting Using Exponential Smoothing Method: A Comparative Analysis

Mahajan, S., Chen, L., & Tsai, T.-C. (2018) · Sensors · doi:10.3390/s18103223 · Cited by 80

Ensembles of incremental learners to detect anomalies in ad hoc sensor networks

Bosman, H. H. W. J., Iacca, G., Tejada, A., Wörtche, H. J., & Liotta, A. (2015) · Ad Hoc Networks · doi:10.1016/j.adhoc.2015.07.013 · Cited by 79

Anomaly Detection in High-Dimensional Data

Talagala, P. D., Hyndman, R. J., & Smith‐Miles, K. (2020) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1807997 · Cited by 77

ITrust: An Anomaly-Resilient Trust Model Based on Isolation Forest for Underwater Acoustic Sensor Networks

Du, J., Han, G., Lin, C., & Martínez-García, M. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3028369 · Cited by 71

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

(2015) · doi:10.1109/aina34533.2015 · Cited by 71

Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings

Kurt, M. N., Yılmaz, Y., & Wang, X. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2970410 · Cited by 69

Blockchain-based anomaly detection of electricity consumption in smart grids

Li, M., Zhang, K., Liu, J., Gong, H., & Zhang, Z. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.07.020 · Cited by 67

Anomaly detection for Smart City applications over 5G low power wide area networks

Santos, J., Leroux, P., Wauters, T., Volckaert, B., & Turck, F. D. (2018) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2018.8406257 · Cited by 65

SMS: A Secure Healthcare Model for Smart Cities

Tripathi, G., Ahad, M. A., & Paiva, S. (2020) · Electronics · doi:10.3390/electronics9071135 · Cited by 64

Lightweight collaborative anomaly detection for the IoT using blockchain

Mirsky, Y., Golomb, T., & Elovici, Y. (2020) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2020.06.008 · Cited by 63

Edge Intelligence (EI)-Enabled HTTP Anomaly Detection Framework for the Internet of Things (IoT)

An, Y., Yu, F. R., Li, J., Chen, J., & Leung, V. C. M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3024645 · Cited by 63

DAD: A Distributed Anomaly Detection system using ensemble one-class statistical learning in edge networks

Moustafa, N., Keshk, M., Choo, K. R., Lynar, T., Camtepe, S. A., & Whitty, M. T. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.01.011 · Cited by 58

DS2PM: A Data-Sharing Privacy Protection Model Based on Blockchain and Federated Learning

Chen, Y., Li, J., Wang, F., Yue, K., Li, Y., Xing, B., Zhang, L., & Chen, L. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3134755 · Cited by 58

READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

Yahyaoui, A., Abdellatif, T., Yangui, S., & Attia, R. (2021) · IEEE Access · doi:10.1109/access.2021.3056149 · Cited by 54

Anomaly Detection using Machine Learning to Discover Sensor Tampering in IoT Systems

Pathak, A. K., Saguna, S., Mitra, K., & Åhlund, C. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500825 · Cited by 53

APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder

Basati, A., & Faghih, M. M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06011-9 · Cited by 50

Robust Anomaly Detection on Unreliable Data

Zhao, Z., Cerf, S., Birke, R., Robu, B., Bouchenak, S., Mokhtar, S. B., & Chen, L. Y. (2019) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2019.00068 · Cited by 48

RAPPER: Ransomware Prevention via Performance CountersPreprint

Alam, M., Sinha, S., Bhattacharya, S., Dutta, S., Mukhopadhyay, D., & Chattopadhyay, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01712 · Cited by 47

Lightweight Photoplethysmography Quality Assessment for Real-time IoT-based Health Monitoring using Unsupervised Anomaly Detection

Mahmoudzadeh, A., Azimi, I., Rahmani, A. M., & Liljeberg, P. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.03.025 · Cited by 47

IoT based intelligence for proactive waste management in Quick Service Restaurants

Aytaç, K., & Korçak, Ö. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125401 · Cited by 46

Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Liu, C., Su, X.-L., & Li, C. (2021) · Electronics · doi:10.3390/electronics10030302 · Cited by 45

Green Energy Efficient Routing with Deep Learning Based Anomaly Detection for Internet of Things (IoT) Communications

Lydia, E. L., Jovith, A. A., Devaraj, A. F. S., Seo, C., & Joshi, G. P. (2021) · Mathematics · doi:10.3390/math9050500 · Cited by 45

Distributed Anomaly Detection Using Minimum Volume Elliptical Principal Component Analysis

O’Reilly, C., Gluhak, A., & Imran, M. A. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2555804 · Cited by 42

Household differentiation and residential electricity demand in Korea

Ryu, J.-Y., Kim, D.-W., & Kim, M. (2021) · Energy Economics · doi:10.1016/j.eneco.2020.105090 · Cited by 41

Scalable and Robust Outlier Detector using Hierarchical Clustering and Long Short-Term Memory (LSTM) Neural Network for the Internet of Things

Shukla, R. M., & Sengupta, S. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100167 · Cited by 38

An Efficient One-Class SVM for Anomaly Detection in the Internet of ThingsPreprint

Yang, K., Kpotufe, S., & Feamster, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.11146 · Cited by 38

Soft computing for anomaly detection and prediction to mitigate IoT-based real-time abuse

Bhatia, M. P. S., & Sangwan, S. R. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01567-8 · Cited by 36

Anomaly Detection Models for Detecting Sensor Faults and Outliers in the IoT - A Survey

Gaddam, A., Wilkin, T., & Angelova, M. N. (2019) · International Conference on Sensing Technology · doi:10.1109/icst46873.2019.9047684 · Cited by 35

A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process

Huang, S., Guo, Y. H., Yang, N., Zha, S., Liu, D., & Fang, W. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01690-y · Cited by 35

Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing

Ngo, M. V., Luo, T., Chaouchi, H., & Quek, T. Q. S. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00191 · Cited by 34

Encyclopedia of Wireless Networks

Shen, X., Lin, X. S., & Zhang, K. (2020) · Springer eBooks · doi:10.1007/978-3-319-78262-1 · Cited by 34

Temperature anomaly detection for electric load forecasting

Sobhani, M., Hong, T., & Martin, C. (2019) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.04.022 · Cited by 32

Anomaly detection in smart houses: Monitoring elderly daily behavior for fall detecting

Galvão, Y. M., Albuquerque, V. A., Fernandes, B., & Valença, M. (2017) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci.2017.8285701 · Cited by 31

IoT anomaly detection method in intelligent manufacturing industry based on trusted evaluation

Wang, C. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04274-0 · Cited by 29

IoT-CAD

Yasaei, R., Hernandez, F., & Faruque, M. A. A. (2020) · doi:10.1145/3400302.3415672 · Cited by 29

AnoML-IoT: An end to end re-configurable multi-protocol anomaly detection pipeline for Internet of Things

Kayan, H., Majib, Y., Alsafery, W., Barhamgi, M., & Perera, C. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100437 · Cited by 29

Image anomaly detection for IoT equipment based on deep learning

Hou, R., Pan, M., Zhao, Y., & Yang, Y. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.102599 · Cited by 26

Monotone Split and Conquer for Anomaly Detection in IoT Sensory Data

Dang, T.-B., Le, D.-T., Nguyễn, T. D., Kim, M., & Choo, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3073705 · Cited by 25

Anomaly Detection using IoT Sensor-Assisted ConvLSTM Models for Connected Vehicles

Zekry, A., Sayed, A. E., Moussa, M., & Elhabiby, M. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-spring51267.2021.9449086 · Cited by 25

HADIoT: A Hierarchical Anomaly Detection Framework for IoT

Chang, H., Feng, J., & Duan, C. (2020) · IEEE Access · doi:10.1109/access.2020.3017763 · Cited by 24

Lifelong Condition Monitoring Based on NB-IoT for Anomaly Detection of Machinery Equipment

Li, C., Mo, L., Tang, H., & Yan, R. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.07.010 · Cited by 22

An efficient approach for clustering and classification for fraud detection using bankruptcy data in IoT environment

Srikanth, P. (2021) · International Journal of Information Technology · doi:10.1007/s41870-021-00756-1 · Cited by 22

The State of IoT Security: Unequivocal Appeal to Cybercriminals, Onerous to Defenders

Alsheikh, M., Konieczny, L., Prater, M., et al. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3079635 · Cited by 22

TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks

Shahraki, A., Taherkordi, A., & Haugen, Ø. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108125 · Cited by 21

Online Anomaly Detection in Multivariate Settings

Mozaffari, M., & Yılmaz, Y. (2019) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2019.8918893 · Cited by 15

Advanced Traffic Violation Control and Penalty System using IoT and Image Processing Techniques

Asoba, S., Supekar, S., Tonde, T., & Siddiqui, J. A. (2020) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia48430.2020.9074949 · Cited by 13

An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis

Dymora, P., & Mazurek, M. (2020) · Applied Sciences · doi:10.3390/app10093277 · Cited by 13

On Detecting IoT Power Signature Anomalies using Hidden Markov Model (HMM)

Fouad, M. A., & Abdel-Hamid, A. T. (2019) · doi:10.1109/icm48031.2019.9021483 · Cited by 12

Anomaly Detection in a Logistic Operating System Using the Mahalanobis–Taguchi Method

Asakura, T., Yashima, W., Suzuki, K., & Shimotou, M. (2020) · Applied Sciences · doi:10.3390/app10124376 · Cited by 12

SEM: Stacking Ensemble Meta-Learning for IOT Security Framework

Mishra, D., Naik, B., Dash, P. B., & Nayak, J. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-05187-x · Cited by 12

Interpreting Deep Learning: The Machine Learning Rorschach Test?Preprint

Charles, A. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00148 · Cited by 12

Anomaly Detection for Non-Stationary and Non-Periodic Univariate Time Series

Li, Y., & Jiang, J. (2020) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.1109/ecice50847.2020.9301943 · Cited by 11

Detecting Anomalous Transactions via an IoT Based Application: A Machine Learning Approach for Horse Racing Betting

Min, M., Lee, J. J., Park, H., & Lee, K. (2021) · Sensors · doi:10.3390/s21062039 · Cited by 11

Time dependent anomaly detection system for smart environment using probabilistic timed automaton

Venkatraman, S., Muthusamy, P., Balusa, B. C., Jayasankar, T., Kavithaa, G., Sekar, K. R., & Bharatiraja, C. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02769-3 · Cited by 8

The Publish or Perish tutorial: 80 easy tips to get the best out of the Publish or Perish software

Harzing, A.-W. K. (2016) · Cited by 7

Spatio-Temporal Correlation based Anomaly Detection and Identification Method for IoT Sensors

Cui, Y., Bao, J. W., Wang, J., Zhang, Q., & Jiang, X. (2019) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais46528.2019.9074607 · Cited by 6

A Service Selection Framework for Anomaly Detection in IoT Stream Data

Yang, Z., Ding, W., Zhang, Z., Li, H., Zhang, M., & Liu, C. (2020) · International Conference on Service Science (ICSS) · doi:10.1109/icss50103.2020.00032 · Cited by 6

IoT Aided Smart Home Architecture for Anomaly Detection

Arif, İ., & Ackovska, N. (2021) · Internet of things · doi:10.1007/978-3-030-67197-6_1 · Cited by 5

Anomaly Detection for IoT Systems

Lin, X.-X., Yeh, E.-H., & Lin, P. (2020) · Springer eBooks · doi:10.1007/978-3-319-78262-1_183 · Cited by 4

Electricity Prediction under Edge Devices Based on Sparse Anomaly Perception

Yang, J., Xu, A., Zeng, Y., Li, L. C., Jiang, Y., Zhang, Y., & Wen, H. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1659/1/012015 · Cited by 3

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 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

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

Data Anomaly Detection in the Internet of Things: A Review of Current Trends and Research Challenges

Yang, M., & Zhang, J. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140901 · Cited by 24

Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) EnvironmentPreprint

Achiluzzi, E., Li, M., Georgy, M. F. A., & Kashef, R. F. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.00134 · Cited by 2

A Survey of Applications for Anomaly Detection in the IoT: Methods, New Perspectives, and Future

Wang, Y., Guo, R., & Min, P. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831268 · Cited by 2