Analysis of anomaly detection method for Internet of things based on deep learning

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Drought forecasting using feed-forward recursive neural network

Mishra, A. K., & Desai, V. R. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.04.017 · Cited by 374 · see citing works

Robust Sparse Linear Discriminant Analysis

Wen, J., Fang, X., Cui, J., Fei, L., Yan, K., Chen, Y., & Xu, Y. D. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2799214 · Cited by 373 · see citing works

Privacy and consumer risks in cloud computing

Svantesson, D. J. B., & Clarke, R. (2010) · Computer law & security review · doi:10.1016/j.clsr.2010.05.005 · Cited by 227 · see citing works

Research on prediction model of geotechnical parameters based on BP neural network

Cui, K., & Jing, X. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3902-6 · Cited by 218 · see citing works

Recursive Neural Network Rule Extraction for Data With Mixed Attributes

Setiono, R., Baesens, B., & Mues, C. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2007.908641 · Cited by 139 · see citing works

Bolt loosening analysis and diagnosis by non-contact laser excitation vibration tests

Huda, F., KAJIWARA, I., Hosoya, N., & KAWAMURA, S. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.05.023 · Cited by 134 · see citing works

Multi-sink distributed power control algorithm for Cyber-physical-systems in coal mine tunnels

Wei, W., Xia, X., Woźniak, M., Fan, X., Damaševičius, R., & Li, Y. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.04.017 · Cited by 132 · see citing works

Oxygen Reduction Reaction on Classically Immiscible Bimetallics: A Case Study of RhAu

Li, H., Luo, L., Kunal, P., Bonifacio, C. S., Duan, Z., Yang, J. C., Humphrey, S. M., Crooks, R. M., & Henkelman, G. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.7b10974 · Cited by 129 · see citing works

Research and Application of Acoustic Emission Signal Processing Technology

Zhao, L., Kang, L., & Yao, S. (2018) · IEEE Access · doi:10.1109/access.2018.2886095 · Cited by 91 · see citing works

Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communications

Xiong, W., Hu, H., Xiong, N. N., Yang, L. T., Peng, W.-C., Wang, X., & Qu, Y. (2013) · Information Sciences · doi:10.1016/j.ins.2013.04.009 · Cited by 90 · see citing works

Economic Denial of Sustainability (EDoS) in Cloud Services using HTTP and XML based DDoS Attacks

VivinSandar, S., & Shenai, S. (2012) · International Journal of Computer Applications · doi:10.5120/5807-8063 · Cited by 83 · see citing works

Security and Privacy of Smart Home Systems Based on the Internet of Things and Stereo Matching Algorithms

Yang, A., Zhang, C., Chen, Y., Zhuansun, Y., & Liu, H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2946214 · Cited by 82 · see citing works

Research on logistics supply chain of iron and steel enterprises based on block chain technology

Yang, A.-M., Li, Y., Liu, C.-S., Li, J., Zhang, Y.-Z., & Wang, J.-H. (2019) · Future generations computer systems · doi:10.1016/j.future.2019.07.008 · Cited by 71 · see citing works

Risk Assessment of Hypertension in Steel Workers Based on LVQ and Fisher-SVM Deep Excavation

Wu, J., Wei, W., Zhang, L., Wang, J., Damaševičius, R., Li, J., Wang, H., Wang, G., Zhang, X., Yuan, J., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2899625 · Cited by 67 · see citing works

Experimental Study on Calcining Process of Secondary Coated Ceramsite Solidified Chromium Contaminated Soil

Yang, Y., Li, H., Zheng, W., Bai, Y., Liu, Z.-M., & Zhang, J. (2019) · Science of Advanced Materials · doi:10.1166/sam.2019.3453 · Cited by 54 · see citing works

Modeling network traffic for traffic matrix estimation and anomaly detection based on Bayesian network in cloud computing networks

Nie, L., Jiang, D., & Lv, Z. (2016) · Annals of Telecommunications · doi:10.1007/s12243-016-0546-3 · Cited by 46 · see citing works

Synergistic Effect Between Nano-Sb2O3 and Brominated Epoxy Resin on the Flame Retardancy of Poly(butylene terephthalate)

Niu, L., Xu, J., Yang, W., Kang, C., Ji-qiang, M., & Su, J. (2019) · Science of Advanced Materials · doi:10.1166/sam.2019.3434 · Cited by 42 · see citing works

Eucalyptus: an open-source cloud computing infrastructure

Nurmi, D. C., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., & Zagorodnov, D. (2009) · Journal of Physics Conference Series · doi:10.1088/1742-6596/180/1/012051 · Cited by 32 · see citing works

Special issue on machine learning applications for self-organized wireless sensor networks

Elhoseny, M., Yuan, X., & Gunasekaran, M. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3926-y · Cited by 29 · see citing works

Feature Extraction and Area Identification of Wireless Channel in Mobile Communication

Li, J., Zhang, L., Feng, X., Jia, K., & Kong, F. (2019) · 網際網路技術學刊 · Cited by 28 · see citing works

Application of Clustering Analysis in Brain Gene Data Based on Deep Learning

Suo, Y., Liu, T., Jia, X., & Yu, F. (2018) · IEEE Access · doi:10.1109/access.2018.2886425 · Cited by 21 · see citing works

Structure Regularized Traffic Monitoring for Traffic Matrix Estimation and Anomaly Detection by Link-Load Measurements

Zhang, Q., & Chu, T. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2016.2599426 · Cited by 18 · see citing works

On the prediction of chemical exergy of organic substances using least square support vector machine

Wu, H. Q., Yu, Y., Fu, H., & Zhang, L. (2017) · Energy Sources Part A Recovery Utilization and Environmental Effects · doi:10.1080/15567036.2017.1403516 · Cited by 10 · see citing works

Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay

Wang, S., Feng, J. Y., & Zhao, Q. (2012) · Chinese Physics B · doi:10.1088/1674-1056/21/12/120701 · Cited by 8 · see citing works

A Network-Based Anomaly Detection System Based on Three Different Network Traffic Characteristics

Yuji, Y., Waizumi, Yohei, Yoshiaki, & Nemoto (2012) · 通讯和计算机：中英文版 · Cited by 5 · see citing works

A NEW STLF APPROACH BASED ON THE FUSION OF OPTIMAL NEIGHBOR POINTS IN PHASE SPACE AND THE RECURSIVE NEURAL NETWORK

Zhang, Z. (2003) · Proceedings of the CSEE · Cited by 4 · see citing works

Virtual network routing in cloud computing environment

Ponnusamy, P. P., & Abinaya, S. (2016) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci.2016.7479968 · Cited by 4 · see citing works

A Network Flow Approach in Cloud Computing

Feizi, S., Zhang, A., & Médard, M. (2013) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2013.6624265 · Cited by 3 · see citing works

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad, Z., Khan, A. S., Shiang, C. W., Abdullah, J., & Ahmad, F. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4150 · Cited by 1,313 · see citing works

Anomaly based network intrusion detection for IoT attacks using deep learning technique

Sharma, B. L., Sharma, L., Lal, C., & Roy, S. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108626 · Cited by 267 · see citing works

Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree

Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265 · see citing works

Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach

Sharma, B. L., Sharma, L., Lal, C., & Roy, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121751 · Cited by 298 · see citing works

Artificial intelligence advances in anomaly detection for telecom networks

Edozie, E., Shuaibu, A. N., Sadiq, B. O., & Ukagwu, K. J. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11108-x · Cited by 93 · see citing works

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Elhanashi, A., Dini, P., Saponara, S., & Zheng, Q. (2023) · Electronics · doi:10.3390/electronics12244925 · Cited by 89 · see citing works

Deep learning in medical imaging: A brief review

Serte, S., Serener, A., & Al-Turjman, F. M. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4080 · Cited by 82 · see citing works

Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets

Hajj, S., Sibai, R. E., Abdo, J. B., Demerjian, J., Makhoul, A., & Guyeux, C. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4240 · Cited by 65 · see citing works

An Anomaly Detection Model Based on Deep Auto-Encoder and Capsule Graph Convolution via Sparrow Search Algorithm in 6G Internet of Everything

Yin, S., Li, H., Laghari, A. A., Gadekallu, T. R., Sampedro, G. A., & Almadhor, A. S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3353337 · Cited by 48 · see citing works

Improved Transformer-Based Privacy-Preserving Architecture for Intrusion Detection in Secure V2X Communications

Lai, Q., Chen, X., Chen, J., Wang, W. W., Chen, J., Gadekallu, T. R., Cai, M., & Hu, X. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3324081 · Cited by 35 · see citing works

Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problem

Pimsarn, C., Boongoen, T., Iam-On, N., Naik, N., & Yang, L. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00739-0 · Cited by 22 · see citing works

Autonomous LLM Agent: A Memory-Augmented, Edge-Optimized SHAP Explanations With Zero-Day Attack Resilience in IoT/Industrial IoT Networks

Saheed, Y. K., & Chukwuere, J. E. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3648649 · Cited by 16 · see citing works

MS‐ADS : Multistage Spectrogram image‐based Anomaly Detection System for IoT security

Ahmad, Z., Khan, A. S., Zen, K. B., & Ahmad, F. (2023) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4810 · Cited by 14 · see citing works

Detection and Correction of Abnormal IoT Data from Tea Plantations Based on Deep Learning

Wang, R., Feng, J., Wu, Z., Liu, B., Wang, T., Zhang, C., Xu, S., Zhang, L., Zuo, G., Lv, Y., et al. (2023) · Agriculture · doi:10.3390/agriculture13020480 · Cited by 11 · see citing works

Towards establishing intelligent multi-domain edge orchestration for highly distributed immersive services: a virtual touring use case

Benmerar, T. Z., Θεοδωρόπουλος, Θ., Fevereiro, D., Rosa, L., Rodrigues, J., Taleb, T., Barone, P., Giuliani, G., Tserpes, K., & Cordeiro, L. (2024) · Cluster Computing · doi:10.1007/s10586-024-04413-7 · Cited by 9 · see citing works

A Comprehensive Survey for Machine Learning and Deep Learning Applications for Detecting Intrusion Detection

Surakhi, O., Mora, A. M., Jamoos, M., & AlKhanafseh, M. (2021) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit53391.2021.9677375 · Cited by 6 · see citing works

Abnormal Traffic Detection Method of Internet of Things Based on Deep Learning in Edge Computing Environment

Qiu, L., & Wang, L. (2023) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126623502833 · Cited by 5 · see citing works

Cyber Edge: Mitigating Cyber-Attacks in Edge Computing Using Intrusion Detection System

Al-Aqqad, W., Nijim, M., Onyeakazi, U., & Al-Bataineh, H. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-56950-0_25 · Cited by 3 · see citing works

Classification of Adversarial Attacks Using Ensemble Clustering Approach

Tatongjai, P., Boongoen, T., Iam-On, N., Naik, N., & Yang, L. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.024858 · Cited by 3 · see citing works

Data Analytics and Modeling in IoT-Fog Environment for Resourceconstrained IoT-Applications: A Review

Farooq, O., & Singh, P. (2021) · Recent Advances in Computer Science and Communications · doi:10.2174/2666255814666210715161630 · Cited by 3 · see citing works

Intrusion Detection Systems: Survey, Taxonomy and Challenges

Wu, Z., Huang, J., Han, W., & Zhang, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-4522-7_5 · Cited by 2 · see citing works

To Evaluate and Analyze the Performance of Anomaly Detection in Cloud of Things

Garg, U., Sivaraman, H., Bamola, A., & Kumari, P. (2022) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt54827.2022.9984316 · Cited by 2 · see citing works

Autoencoder-Based System for Detecting Anomalies in Pelletizer Melt Processes

Zhu, M., Zhang, G., Feng, L., Li, X., & Lv, X. (2024) · Sensors · doi:10.3390/s24227277 · Cited by 1 · see citing works

Securing End-to-End Reinforcement Learning-Driven Autonomous Driving: A Control Command Utility-Based Intrusion Response System

Zhang, Q., Yoon, H. J., Moore, T. J., Yoon, S., Kim, D. S., Lim, H., Nelson, F. F., & Cho, J.-H. (2025) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2025.3598768 · Cited by 1 · see citing works

Visual perception and deep learning in smart homes and the Internet of Things

Li, J. (2024) · Highlights in Science Engineering and Technology · doi:10.54097/bcvmxs60 · Cited by 1 · see citing works

Collaborative Defense System for Security Policies in Cloud-Edge Scenarios Based on Virtualization

Li, Y., Yao, L., Fu, Z., Wang, H., & Liang, W. G. (2025) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda64898.2025.11030532 · Cited by 1 · see citing works

Multi-channel data flow software fault detection for social internet of things with system assurance concerns

You, L. (2023) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-01878-4

Literature Review on Network Security Approaches using Machine Learning Models

Deepa, V. P., & Sukumar, R. (2023) · International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM) · doi:10.1109/ic-rvitm60032.2023.10434976

Detection and Classification of Abnormal Data Based on Deep Learning and Improved Algorithms

Li, M. (2024) · International Conference on Computers, Information Processing and Advanced Education (CIPAE) · doi:10.1109/cipae64326.2024.00060

BedIDS: An Effective Network Anomaly Detection Method by Fusing Behavior Evolution characteristics

Liu, Z., Hu, C., Shan, C., & Yi, J. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00212

Intelligent Feature Optimization for Anomaly Detection in IoT-Enabled Smart Urban Networks

Kamath, P. A., Sharma, B., & Sharma, L. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-22065-3_36

A Hybrid Model for Anomaly Detection in Cloud Using Deep Learning

Ravinarayana, B. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem25054 · Cited by 1 · see citing works

Inferring Anomalies from Cloud Metrics Using Recurrent Neural Networks

Chouliaras, S., & Sotiriadis, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-84913-9_14 · Cited by 1 · see citing works

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 · see citing works

A Deep Learning-based Model for Anomaly Data Detection in Cloud Failure Scenarios

Li, Z., Wang, H., Zhang, X., Han, X., Li, Y., & Long, Y. (2024) · International Conference on Communication Technology and Information Technology (ICCTIT) · doi:10.1109/icctit64404.2024.10928416

Anomaly Detection of Cloud Network Resource State Based on Deep Learning

Shi, J., Lai, F., Li, W., Wang, H., Zhang, X., & Li, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-0852-1_41 · Cited by 1 · see citing works