Hybrid intrusion detection system for edge-based IIoT relying on machine-learning-aided detection

HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection

Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., & Zhu, M. (2017) · IEEE Access · doi:10.1109/access.2017.2780250 · Cited by 664 · see citing works

Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2890685 · Cited by 560 · see citing works

Deep Android Malware Detection

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Routing Attacks and Countermeasures in the RPL-Based Internet of Things

Wallgren, L., Raza, S., & Voigt, T. (2013) · International Journal of Distributed Sensor Networks · doi:10.1155/2013/794326 · Cited by 483 · see citing works

Intrusion Detection using Naive Bayes Classifier with Feature Reduction

Mukherjee, S., & Sharma, N. (2012) · Procedia Technology · doi:10.1016/j.protcy.2012.05.017 · Cited by 477 · see citing works

Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective

Wang, K., Wang, Y., Sun, Y., Guo, S., & Wu, J. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.1600399cm · Cited by 435 · see citing works

Satellite-5G Integration: A Network Perspective

Giambene, G., Kota, S., & Pillai, P. (2018) · IEEE Network · doi:10.1109/mnet.2018.1800037 · Cited by 341 · see citing works

Security in Mobile Edge Caching with Reinforcement Learning

Xiao, L., Wan, X., Dai, C., Du, X., Chen, X., & Guizani, M. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1700291 · Cited by 228 · see citing works

A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology

Le, A., Loo, J. K. K., Chai, K. K., & Aiash, M. (2016) · Information · doi:10.3390/info7020025 · Cited by 205 · see citing works

A hybrid network intrusion detection technique using random forests

Zhang, J., & Zulkernine, M. (2006) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2006.7 · Cited by 200 · see citing works

Host Based Intrusion Detection System with Combined CNN/RNN Model

Chawla, A., Lee, B., Fallon, S., & Jacob, P. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-13453-2_12 · Cited by 161 · see citing works

A Malicious Pattern Detection Engine for Embedded Security Systems in the Internet of Things

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Game-Theory-Based Active Defense for Intrusion Detection in Cyber-Physical Embedded Systems

Wang, K., Du, M., Yang, D., Zhu, C., Shen, J., & Zhang, Y. (2016) · ACM Transactions on Embedded Computing Systems · doi:10.1145/2886100 · Cited by 82 · see citing works

Attack Detection and Distributed Forensics in Machine-to-Machine Networks

Wang, K., Du, M., Sun, Y., Vinel, A. V., & Zhang, Y. (2016) · IEEE Network · doi:10.1109/mnet.2016.1600113nm · Cited by 63 · see citing works

Intrusion Detection Systems using Linear Discriminant Analysis and Logistic Regression

Subba, B., Biswas, S., & Karmakar, S. (2015) · IEEE India Conference · doi:10.1109/indicon.2015.7443533 · Cited by 42 · see citing works

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

Tange, K., De Donno, M., Fafoutis, X., & Dragoni, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011208 · Cited by 410 · see citing works

A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

Zhao, R., Gui, G., Xue, Z., Yin, J., Ohtsuki, T. O., Adebisi, B., & Gacanin, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3119055 · Cited by 230 · see citing works

Yet Another Traffic Classifier: A Masked Autoencoder Based Traffic Transformer with Multi-Level Flow Representation

Zhao, R.-J., Zhan, M.-W., Deng, X.-W., Wang, Y., Wang, Y.-J., Gui, G., & Xue, Z. (2023) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25674 · Cited by 212 · see citing works

A survey on deep learning for cybersecurity: Progress, challenges, and opportunities

Macas, M., Wu, C., & Fuertes, W. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109032 · Cited by 192 · see citing works

Semisupervised Federated-Learning-Based Intrusion Detection Method for Internet of Things

Zhao, R.-J., Wang, Y.-J., Xue, Z., Ohtsuki, T., Adebisi, B., & Gui, G. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3175918 · Cited by 131 · see citing works

Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges

Bao, G., & Guo, P. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00377-4 · Cited by 131 · see citing works

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Mohy-eddine, M., Guezzaz, A., Benkirane, S., Azrour, M., & Farhaoui, Y. (2023) · Big Data Mining and Analytics · doi:10.26599/bdma.2022.9020032 · Cited by 139 · see citing works

A systematic review on Deep Learning approaches for IoT security

Aversano, L., Bernardi, M. L., Cimitile, M., & Pecori, R. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100389 · Cited by 128 · see citing works

Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review

Kheddar, H., Himeur, Y., & Awad, A. I. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103760 · Cited by 125 · see citing works

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez, T., Xu, L., & McArdle, G. (2021) · IEEE Access · doi:10.1109/access.2021.3069137 · Cited by 124 · see citing works

Cybersecurity for Industrial IoT (IIoT): Threats, countermeasures, challenges and future directions

Mekala, S. H., Baig, Z., Anwar, A., & Zeadally, S. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.06.020 · Cited by 111 · see citing works

Intelligent approaches toward intrusion detection systems for Industrial Internet of Things: A systematic comprehensive review

Nuaimi, M., Fourati, L. C., & Hamed, B. B. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103637 · Cited by 99 · see citing works

Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

Li, K., Zhou, H., Tu, Z., Wang, W., & Zhang, H. (2020) · IEEE Access · doi:10.1109/access.2020.3041641 · Cited by 96 · see citing works

An Efficient Intrusion Detection Method Based on Dynamic Autoencoder

Zhao, R., Yin, J., Xue, Z., Gui, G., Adebisi, B., Ohtsuki, T. O., Gacanin, H., & Sari, H. (2021) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2021.3077946 · Cited by 93 · see citing works

Research on Intrusion Detection Method Based on Transformer and CNN-BiLSTM in Internet of Things

Zhang, C., Li, J., Wang, N., & Zhang, D. (2025) · Sensors · doi:10.3390/s25092725 · Cited by 88 · see citing works

An effective intrusion detection approach based on ensemble learning for IIoT edge computing

Mohy-eddine, M., Guezzaz, A., Benkirane, S., & Azrour, M. (2022) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-022-00456-9 · Cited by 88 · see citing works

An edge based hybrid intrusion detection framework for mobile edge computing

Singh, A. P., Chatterjee, K., & Satapathy, S. C. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00498-4 · Cited by 82 · see citing works

Research trends in deep learning and machine learning for cloud computing security

Alzoubi, Y. I., Mishra, A., & Topcu, A. E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10776-5 · Cited by 78 · see citing works

MT-FlowFormer: A Semi-Supervised Flow Transformer for Encrypted Traffic Classification

Zhao, R., Deng, X., Yan, Z., Ma, J., Xue, Z., & Wang, Y. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539314 · Cited by 64 · see citing works

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 · see citing works

A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection

Vitorino, J., Andrade, R., Praça, I., Sousa, O., & Maia, E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08147-7_13 · Cited by 65 · see citing works

Intrusion Detection in IoT-Based Smart Grid Using Hybrid Decision Tree

Taghavinejad, S. M., Taghavinejad, M., Shahmiri, L., Zavvar, M. H., & Zavvar, M. H. (2020) · International Conference on Web Research (ICWR) · doi:10.1109/icwr49608.2020.9122320 · Cited by 58 · see citing works

A Genetic Algorithm- and t-Test-Based System for DDoS Attack Detection in IoT Networks

Saiyed, M. F., & Al‐Anbagi, I. (2024) · IEEE Access · doi:10.1109/access.2024.3367357 · Cited by 57 · see citing works

Towards adversarial realism and robust learning for IoT intrusion detection and classification

Vitorino, J., Praça, I., & Maia, E. (2023) · Annals of Telecommunications · doi:10.1007/s12243-023-00953-y · Cited by 61 · see citing works

An explainable efficient flow-based Industrial IoT intrusion detection system

Alani, M. M. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108732 · Cited by 55 · see citing works

APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

Javed, S. H., Ahmad, M. B., Asif, M., Akram, W., Mahmood, K., Das, A. K., & Shetty, S. S. (2023) · IEEE Access · doi:10.1109/access.2023.3291599 · Cited by 49 · see citing works

Particle swarm optimization-enhanced machine learning and deep learning techniques for Internet of Things intrusion detection

Benmalek, M., & Seddiki, A. (2025) · Data Science and Management · doi:10.1016/j.dsm.2025.02.005 · Cited by 55 · see citing works

LKD-STNN: A Lightweight Malicious Traffic Detection Method for Internet of Things Based on Knowledge Distillation

Zhu, S., Xu, X., Zhao, J., & Xiao, F. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3310794 · Cited by 43 · see citing works

Leveraging AI for Intrusion Detection in IoT Ecosystems: A Comprehensive Study

Sharma, S. B., & Bairwa, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3550392 · Cited by 42 · see citing works

Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

Eid, A. M., Soudan, B., Nassif, A. B., & Injadat, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09857-x · Cited by 40 · see citing works

PCNNCEC: Efficient and Privacy-Preserving Convolutional Neural Network Inference Based on Cloud-Edge-Client Collaboration

Wang, J., He, D., Castiglione, A., Gupta, B. B., Karuppiah, M., & Wu, L. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3177755 · Cited by 38 · see citing works

Enhanced Few-Shot Malware Traffic Classification via Integrating Knowledge Transfer With Neural Architecture Search

Zhang, X., Wang, Q., Qin, M., Wang, Y., Ohtsuki, T. O., Adebisi, B., Sari, H., & Gui, G. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3396624 · Cited by 38 · see citing works

FOG-Empowered Augmented-Intelligence-Based Proactive Defensive Mechanism for IoT-Enabled Smart Industries

Javeed, D., Gao, T., Saeed, M. S., & Khan, M. T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288563 · Cited by 37 · see citing works

A Blockchain-Based Intrusion Detection System Using Viterbi Algorithm and Indirect Trust for IIoT Systems

Rathee, G., Kerrache, C. A., & Ferrag, M. A. (2022) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan11040071 · Cited by 36 · see citing works

DeepIIoT: An Explainable Deep Learning Based Intrusion Detection System for Industrial IOT

Alani, M. M., Damiani, E., & Ghosh, U. (2022) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw56584.2022.00040 · Cited by 35 · see citing works

Review on intrusion detection system for IoT/IIoT -brief study

Bansal, K., & Singhrova, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16395-6 · Cited by 37 · see citing works

Intrusion Detection Systems for Industrial Internet of Things: A Survey

Hamouda, D., Ferrag, M. A., Benhamida, N., & Séridi, H. (2021) · International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS) · doi:10.1109/ictaacs53298.2021.9715177 · Cited by 33 · see citing works

An Efficient and Lightweight Approach for Intrusion Detection based on Knowledge Distillation

Zhao, R., Chen, Y., Wang, Y., Shi, Y., & Xue, Z. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500574 · Cited by 27 · see citing works

Efficient and Intelligent Attack Detection in Software Defined IoT Networks

Zhang, Y., Xu, J., Wang, Z., Geng, R., Choo, K. R., Pérez-Díaz, J. A., & Zhu, D. K. (2020) · International Conference on Embedded Software and Systems · doi:10.1109/icess49830.2020.9301591 · Cited by 26 · see citing works

Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0

Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26 · see citing works

A lightweight IoT intrusion detection method based on two-stage feature selection and Bayesian optimization

Zhang, D., Huang, D., Chen, Y., Lin, S., & Li, C. (2025) · AIMS Electronics and Electrical Engineering · doi:10.3934/electreng.2025017 · Cited by 21 · see citing works

Security analysis of cyber physical system using digital forensic incident response

Binnar, P., Bhirud, S. G., & Kazi, F. (2023) · Cyber Security and Applications · doi:10.1016/j.csa.2023.100034 · Cited by 23 · see citing works

Research on lightweight anomaly detection of multimedia traffic in edge computing

Zhao, X., Huang, G., Jin, J., Gao, L. C., & Li, M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102463 · Cited by 19 · see citing works

Ensemble Filter-based Feature Selection Model for Cyber Attack Detection in Industrial Internet of Things

Arya, L., & Gupta, G. P. (2023) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs57279.2023.10112989 · Cited by 19 · see citing works

A Review of Deep Learning Strategies for Enhancing Cybersecurity in Networks

Bhuvaneshwari, A. J., & Kaythry, P. (2023) · Journal of Scientific & Industrial Research · doi:10.56042/jsir.v82i12.1702 · Cited by 18 · see citing works

Digital Twin-Empowered Smart Attack Detection System for 6G Edge of Things Networks

Yigit, Y., Chrysoulas, C., Yurdakul, G., Maglaras, L. A., & Canberk, B. (2023) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps58843.2023.10465218 · Cited by 18 · see citing works

A Deep Learning Approach for Multiclass Attack Classification in IoT and IIoT Networks Using Convolutional Neural Networks

Seyedkolaei, A. A., Mahmoudi, F., & García, J. (2025) · Future Internet · doi:10.3390/fi17060230 · Cited by 16 · see citing works

Early DGA-based botnet identification: pushing detection to the edges

Zago, M., Pérez, M. G., & Pérez, G. M. (2021) · Cluster Computing · doi:10.1007/s10586-020-03213-z · Cited by 16 · see citing works

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

Qaddoori, S. L., & Ali, Q. I. (2023) · Maǧallaẗ al-handasaẗ al-rāfidayn · doi:10.33899/rengj.2022.134932.1191 · Cited by 15 · see citing works

Explainable Intrusion Detection for Internet of Medical Things

Memon, S. A., Wiil, U. K., & Shaikh, M. (2023) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0012210300003598 · Cited by 15 · see citing works

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Idouglid, L., Tkatek, S., Fayq, K. E., & Guezzaz, A. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i3.pp3512-3521 · Cited by 14 · see citing works

A Lightweight Intrusion Detection System Using a Finite Dirichlet Mixture Model With Extended Stochastic Variational Inference

Lai, Y., Yu, Y., Guan, W., Luo, L., Fan, J., Zhou, N., & Ping, Y. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3391250 · Cited by 13 · see citing works

Application of Edge Computing and Blockchain in Smart Agriculture System

Feng, Y. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/7198624 · Cited by 15 · see citing works

A novel anomaly detection model for the industrial Internet of Things using machine learning techniques

Idouglid, L., Tkatek, S., Fayq, K. E., & Guezzaz, A. (2024) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2024.1.12 · Cited by 12 · see citing works

An Intrusion Detection Method for Cyber Monintoring Using Attention based Hierarchical LSTM

Hou, H., Di, Z., Zhang, M., & Yuan, D. (2022) · IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) · doi:10.1109/bigdatasecurityhpscids54978.2022.00032 · Cited by 12 · see citing works

A Novel HTTP Anomaly Detection Framework Based on Edge Intelligence for the Internet of Things (IoT)

An, Y., Li, J., Yu, F. R., Chen, J., & Leung, V. C. M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.2000249 · Cited by 11 · see citing works

Real-Time Collection Method of Athletes’ Abnormal Training Data Based on Machine Learning

Wang, Y. (2021) · Mobile Information Systems · doi:10.1155/2021/9938605 · Cited by 11 · see citing works

SECBlock-IIoT: A Secure Blockchain-enabled Edge Computing Framework for Industrial Internet of Things

Hosen, A. S. M. S., Sharma, P. K., Puthal, D., Ra, I.-H., & Cho, G. (2023) · doi:10.1145/3591365.3592945 · Cited by 11 · see citing works

An Empirical Evaluation of Machine Learning Algorithms for Intrusion Detection in IIoT Networks

Matke, M., Saurabh, K., & Singh, U. (2023) · IEEE India Conference · doi:10.1109/indicon59947.2023.10440779 · Cited by 11 · see citing works

One Dimensional Conv-BiLSTM Network with Attention Mechanism for IoT Intrusion Detection

Omarov, B., Sailaukyzy, Z., BIGALIYEVA, A., Kereyev, A., Naizabayeva, L., & Dautbayeva, A. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.042469 · Cited by 9 · see citing works

Intelligent Edge Detection of Attacks on IP-based IoT deployments

Harsh, K., Jadhav, A. R., Sasirekha, G. V. K., Bapat, J. L., & Das, D. (2021) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit53463.2021.00036 · Cited by 8 · see citing works

Application of Machine Learning Approaches in Intrusion Detection System

Aziz, Z. A., & Abdulazeez, A. M. (2021) · Journal of Soft Computing and Data Mining · doi:10.30880/jscdm.2021.02.02.001 · Cited by 9 · see citing works

A Federated Learning Framework for IoT: Application to Industry 4.0

Safri, H., Kandi, M. M., Miloudi, Y., Bortolaso, C., Trystram, D., & Desprez, F. (2022) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid54584.2022.00066 · Cited by 8 · see citing works

A Hybrid Machine Learning Approach to Anomaly Detection in Industrial IoT

Jayesh, T. P., Pandiaraj, K., Paul, A., Chandran, R. R., & Menon, P. P. (2023) · International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) · doi:10.1109/access57397.2023.10199711 · Cited by 8 · see citing works

Symmetrical Resilience: Detection of Cyberattacks for SCADA Systems Used in IIoT in Big Data Environments

Okur, C., & Dener, M. (2025) · Symmetry · doi:10.3390/sym17040480 · Cited by 7 · see citing works

On the Suitability of Intrusion Detection System for Wireless Edge Networks

Владимирович, Ш. В., Sokolova, O., & Koo, I. (2021) · Energies · doi:10.3390/en14185954 · Cited by 7 · see citing works

A robust malware traffic classifier to combat security breaches in industry 4.0 applications

Ramaiah, M., Vanmathi, C., & Ravi, V. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7772 · Cited by 6 · see citing works

A Combinatorial Optimization Analysis Method for Detecting Malicious Industrial Internet Attack Behaviors

Zhao, K., Zhang, Z., Choo, K. R., Zhang, Z., & Zhang, T. (2023) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3637554 · Cited by 5 · see citing works

A Dual Active Domain Adaptation Approach With Loss Prediction for IIoT Intrusion Detection Under Imperfect Samples

Ma, W., Lan, X., Liu, R., Wang, J., & Zhou, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3568417 · Cited by 5 · see citing works

A Survey of Machine Learning-based IoT Intrusion Detection Techniques

Long, J., Fang, F., & Luo, H. (2021) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud52277.2021.00009 · Cited by 5 · see citing works

Global Economic Market Forecast and Decision System for IoT and Machine Learning

Liu, B., & Sun, Z. (2022) · Mobile Information Systems · doi:10.1155/2022/8344791 · Cited by 5 · see citing works

A Lightweight Semi-Supervised Learning Method Based on Consistency Regularization for Intrusion Detection

Zhao, R., Tang, T., Gui, G., & Xue, Z. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838567 · Cited by 4 · see citing works

Hidden target recognition method for high-speed network security threats based on attack graph theory

Li-min, S., & Rho, S. (2023) · Journal of High Speed Networks · doi:10.3233/jhs-222048 · Cited by 4 · see citing works

From Concept to Prototype: Developing and Testing GAAINet for Industrial IoT Intrusion Detection

Sithungu, S. P., & Ehlers, E. M. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-57808-3_33 · Cited by 4 · see citing works

A Synoptic Review on Feature Selection and Machine Learning models used for Detecting Cyber Attacks in IoT

Bojarajulu, B., Tanwar, S., & Rana, A. (2021) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon52273.2021.9667573 · Cited by 4 · see citing works

A Novel Intrusion Detection System based on Machine Learning for Internet of Things (IoT) Devices

Chauhan, D., Shah, M., & Joshi, H. (2023) · International Conference on Smart Data Intelligence (ICSMDI) · doi:10.1109/icsmdi57622.2023.00081 · Cited by 3 · see citing works

A Computer Network Security Intrusion Detection Algorithm Based on Deep Learning

Song, X., Song, M., & Li, X. (2024) · International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS) · doi:10.1109/aiars63200.2024.00093 · Cited by 3 · see citing works

ANN-LSTM Assisted Intrusion Detection for Next Generation Core Networks

Kamal, S. J., Karishma, G., Shankar, T., Rajesh, A., Muthiah-Nakarajan, V., & Balaji, S. (2023) · International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) · doi:10.1109/vitecon58111.2023.10157036 · Cited by 3 · see citing works

Metaheuristic-Driven Secure Task Optimization for Consumer Edge Devices

Ullah, F., Mostarda, L., Cacciagrano, D., Chen, C., Amoon, M., & Kumari, S. (2024) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2024.3467768 · Cited by 2 · see citing works

AI2DS: Advanced Deep Autoencoder-Driven Method for Intelligent Network Intrusion Detection Systems

Rahman, M. H., Martinez, L., Mishra, A., Nijim, M., Goyal, A., & Hicks, D. L. (2025) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic63015.2025.10849290 · Cited by 2 · see citing works

A Lightweight and Optimal Defense System for DDoS Attacks in IoMT Networks

Saiyed, M. F., & Al‐Anbagi, I. (2024) · Globecom · doi:10.1109/globecom52923.2024.10901391 · Cited by 2 · see citing works

A Criterion for IDS Deployment on IoT Edge Nodes

Владимирович, Ш. В., Sokolova, O., & Koo, I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58799-4_40 · Cited by 2 · see citing works

DyGRA-Edge: A Dynamic Gradient-Regulated Attention Network for Real-Time Intrusion Detection on Industrial IoT-Edge Nodes

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EAROA-MiXNet: An Efficient Deep Learning-Based Intrusion Detection System for Edge-Based IIoT Environment

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