An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture

Grey Wolf Optimizer

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 20,065 · see citing works

Current research on Internet of Things (IoT) security: A survey

Mohamad Noor, M. B., & Hassan, W. H. (2019) · Computer Networks · doi:10.1016/j.comnet.2018.11.025 · Cited by 989 · see citing works

Analysis of Dimensionality Reduction Techniques on Big Data

Gadekallu, T. R., Maddikunta, P. K. R., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020) · IEEE Access · doi:10.1109/access.2020.2980942 · Cited by 896 · see citing works

Principal component analysis

Kherif, F., & Latypova, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00012-2 · Cited by 726 · see citing works

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

(2017) · doi:10.1109/globecom37483.2017 · Cited by 509 · see citing works

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Bhattacharya, S., Siva Rama Krishnan, S., Maddikunta, P. K. R., Kaluri, R., Singh, S., Gadekallu, T. R., Alazab, M., & Tariq, U. (2020) · Electronics · doi:10.3390/electronics9020219 · Cited by 364 · see citing works

TSDL: A Two-Stage Deep Learning Model for Efficient Network Intrusion Detection

Khan, F. A., Gumaei, A., Derhab, A., & Hussain, A. (2019) · IEEE Access · doi:10.1109/access.2019.2899721 · Cited by 363 · see citing works

Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network

Zhang, Y., Li, P., & Wang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2903723 · Cited by 359 · see citing works

Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection

Salo, F., Nassif, A. B., & Essex, A. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.11.010 · Cited by 305 · see citing works

Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

Farivar, F., Haghighi, M. S., Jolfaei, A., & Alazab, M. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2956474 · Cited by 293 · see citing works

Deep neural networks to predict diabetic retinopathy

Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., & Srivastava, G. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01963-7 · Cited by 285 · see citing works

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Yang, Y., Zheng, K., Wu, C., & Yang, Y. (2019) · Sensors · doi:10.3390/s19112528 · Cited by 274 · see citing works

Intrusion Detection Using Convolutional Neural Networks for Representation Learning

Li, Z., Qin, Z., Huang, K., Yang, X. H., & Ye, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70139-4_87 · Cited by 257 · see citing works

An improved hybrid grey wolf optimization algorithm

Teng, Z.-J., Lv, J.-L., & Guo, L. (2018) · Soft Computing · doi:10.1007/s00500-018-3310-y · Cited by 238 · see citing works

A Deep Learning Method With Filter Based Feature Engineering for Wireless Intrusion Detection System

Kasongo, S. M., & Sun, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2905633 · Cited by 234 · see citing works

A deep auto-encoder based approach for intrusion detection system

Farahnakian, F., & Heikkonen, J. (2018) · International Conference on Advanced Communication Technology · doi:10.23919/icact.2018.8323688 · Cited by 231 · see citing works

IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning

Li, D., Deng, L., Lee, M., & Wang, H. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.006 · Cited by 212 · see citing works

IoMT malware detection approaches: analysis and research challenges

Wazid, M., Das, A. K., Rodrigues, J. J. P. C., Shetty, S., & Park, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2960412 · Cited by 200 · see citing works

The 7th International Conference on Advanced Communication Technology, 2005, ICACT 2005.

(2005) · doi:10.1109/icact10279.2005 · Cited by 190 · see citing works

IoMT-SAF: Internet of Medical Things Security Assessment Framework

Alsubaei, F. S., Abuhussein, A., Shandilya, V., & Shiva, S. G. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100123 · Cited by 179 · see citing works

Review of Security and Privacy for the Internet of Medical Things (IoMT)

Hatzivasilis, G., Soultatos, O., Ioannidis, S., Verikoukis, C., Demetriou, G., & Tsatsoulis, C. (2019) · 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss.2019.00091 · Cited by 172 · see citing works

A deep auto-encoder based approach for intrusion detection system

Farahnakian, F., & Heikkonen, J. (2018) · 2018 20th International Conference on Advanced Communication Technology (ICACT) · doi:10.23919/icact.2018.8323687 · Cited by 146 · see citing works

An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers

Khan, R. U., Zhang, X., Kumar, R., Sharif, A., Golilarz, N. A., & Alazab, M. (2019) · Applied Sciences · doi:10.3390/app9112375 · Cited by 144 · see citing works

An Improved Convolutional Neural Network Model for Intrusion Detection in Networks

Khan, R. U., Zhang, X., Alazab, M., & Kumar, R. (2019) · 2019 Cybersecurity and Cyberforensics Conference (CCC) · doi:10.1109/ccc.2019.000-6 · Cited by 141 · see citing works

Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms

Ren, J., Guo, J., Wang, Q., Huang, Y., Hao, X., & Hu, J. (2019) · Security and Communication Networks · doi:10.1155/2019/7130868 · Cited by 130 · see citing works

A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain

Wang, E. K., Chen, C., Hassan, M. M., & Almogren, A. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.02.054 · Cited by 121 · see citing works

Exact String Matching Algorithms: Survey, Issues, and Future Research Directions

Hakak, S., Kamsin, A., Shivakumara, P., Gilkar, G. A., Khan, W. Z., & Imran, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2914071 · Cited by 109 · see citing works

On Detection of Sybil Attack in Large-Scale VANETs Using Spider-Monkey Technique

Iwendi, C., Uddin, M., Ansere, J. A., Nkurunziza, P., Anajemba, J. H., & Bashir, A. K. (2018) · IEEE Access · doi:10.1109/access.2018.2864111 · Cited by 96 · see citing works

Intrusion detection system using deep learning for in-vehicle security

Zhang, J., Li, F., Zhang, H., Li, R., & Li, Y. (2019) · Ad Hoc Networks · doi:10.1016/j.adhoc.2019.101974 · Cited by 85 · see citing works

P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

Usman, M., Jan, M. A., He, X., & Chen, J. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904349 · Cited by 83 · see citing works

Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet

Möckl, L., Roy, A. R., Petrov, P. N., & Moerner, W. E. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1916219117 · Cited by 78 · see citing works

2018 14th International Conference on Distributed Computing in Sensor Systems (DCOSS)

(2018) · doi:10.1109/dcoss43638.2018 · Cited by 67 · see citing works

Ontology-Based Security Recommendation for the Internet of Medical Things

Alsubaei, F. S., Abuhussein, A., & Shiva, S. G. (2019) · IEEE Access · doi:10.1109/access.2019.2910087 · Cited by 64 · see citing works

Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced FrameworkPreprint

Zhang, H., Yu, X., Ren, P., Luo, C., & Min, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07949 · Cited by 47 · see citing works

A Qualitative Evaluation of IoT-Driven eHealth: Knowledge Management, Business Models and Opportunities, Deployment and Evolution

Lokshina, I. V., & Lanting, C. J. M. (2018) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-94117-2_2 · Cited by 32 · see citing works

A New Approach to Interval-Valued Fuzzy Soft Sets and Its Application in Decision-Making

Tripathy, B. K., Sooraj, T. R., & Mohanty, R. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-2525-9_1 · Cited by 30 · see citing works

A Qualitative Evaluation of IoT-driven eHealth: Knowledge Management, Business Models and Opportunities, Deployment and Evolution

Lokshina, I. V., & Lanting, C. J. M. (2018) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2018.518 · Cited by 27 · see citing works

Deep Learning Approach for IDS

Liu, Z., Mohi-ud-din, G., Zhu, Y., Yan, X., Wang, L., Jiang, Z., & Jianchao, L. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-0637-6_40 · Cited by 25 · see citing works

On Rough Equalities and Rough Equivalences of Sets

Tripathy, B. K., Mitra, A., & Ojha, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88425-5_10 · Cited by 24 · see citing works

IoMT-Based Association Rule Mining for the Prediction of Human Protein Complexes

Sikandar, M., Anwar, W., Almogren, A., Din, I. U., & Guizani, N. (2020) · IEEE Access · doi:10.1109/access.2019.2963797 · Cited by 24 · see citing works

Fourth International Congress on Information and Communication Technology

(2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-32-9343-4 · Cited by 18 · see citing works

Advances in Computational Intelligence

Herrera-Alcántara, O., & Sidorov, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-62434-1 · Cited by 14 · see citing works

ProIDS: Probabilistic Data Structures Based Intrusion Detection System for Network Traffic Monitoring

Gupta, D., Garg, S., Singh, A., Batra, S., Kumar, N., & Obaidat, M. S. (2017) · Globecom · doi:10.1109/glocom.2017.8254439 · Cited by 14 · see citing works

2019 Cybersecurity and Cyberforensics Conference (CCC)

(2019) · doi:10.1109/ccc48145.2019 · Cited by 13 · see citing works

Data-Centric Business and Applications

(2019) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-34706-2 · Cited by 6 · see citing works

A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

Khraisat, A., & Alazab, A. (2021) · Cybersecurity · doi:10.1186/s42400-021-00077-7 · Cited by 540 · see citing works

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

Ullah, I., & Mahmoud, Q. H. (2021) · IEEE Access · doi:10.1109/access.2021.3094024 · Cited by 402 · see citing works

PPSF: a privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities

Kumar, P., Kumar, R., Srivastava, G., Gupta, G. P., Tripathi, R., Gadekallu, T. R., & Xiong, N. N. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3089435 · Cited by 360 · see citing works

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366 · see citing works

Internet of Things intrusion detection systems: a comprehensive review and future directions

Heidari, A., & Jamali, M. A. J. (2022) · Cluster Computing · doi:10.1007/s10586-022-03776-z · Cited by 344 · see citing works

An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks

Kumar, P., Gupta, G. P., & Tripathi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.12.003 · Cited by 317 · see citing works

A systematic literature review for network intrusion detection system (IDS)

Abdulganiyu, O. H., Ait Tchakoucht, T., & Saheed, Y. K. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00682-2 · Cited by 279 · see citing works

Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Chen, J., Yi, C., Okegbile, S. D., Cai, J., & Shen, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3308717 · Cited by 268 · see citing works

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi, F., & Vakanski, A. (2023) · Bioengineering · doi:10.3390/bioengineering10020173 · Cited by 252 · see citing works

Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms

Saheed, Y. K., & Arowolo, M. O. (2021) · IEEE Access · doi:10.1109/access.2021.3128837 · Cited by 246 · see citing works

A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet‐of‐Medical‐Things

Hasan, M. K., Ghazal, T. M., Saeed, R. A., Pandey, B., Gohel, H., Eshmawi, A. A., Abdel‐Khalek, S., & Alkhassawneh, H. M. (2021) · IET Communications · doi:10.1049/cmu2.12301 · Cited by 225 · see citing works

TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning

Kumar, P., Gupta, G. P., & Tripathi, R. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101954 · Cited by 210 · see citing works

Permissioned Blockchain and Deep Learning for Secure and Efficient Data Sharing in Industrial Healthcare Systems

Kumar, R., Kumar, P., Tripathi, R., Gupta, G. P., Islam, A. K. M. N., & Shorfuzzaman, M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3161631 · Cited by 206 · see citing works

IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search Optimization

Fatani, A., Elaziz, M. A., Dahou, A., Al‐qaness, M. A. A., & Lu, S. (2021) · IEEE Access · doi:10.1109/access.2021.3109081 · Cited by 205 · see citing works

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

Kareem, S. S., Mostafa, R. R., Hashim, F. A., & El-Bakry, H. M. (2022) · Sensors · doi:10.3390/s22041396 · Cited by 201 · see citing works

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar, V., Azamathulla, H. M., Sharma, K. V., Mehta, D., & Tota‐Maharaj, K. (2023) · Sustainability · doi:10.3390/su151310543 · Cited by 208 · see citing works

Hand gesture classification using a novel CNN-crow search algorithm

Gadekallu, T. R., Alazab, M., Kaluri, R., Maddikunta, P. K. R., Bhattacharya, S., Lakshmanna, K., & Parimala, M. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00324-x · Cited by 184 · see citing works

Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and Cloud–Fog–Edge architectures

Hernandez-Jaimes, M. L., Martínez-Cruz, A., Ramírez-Gutiérrez, K. A., & Feregrino-Uribe, C. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100887 · Cited by 184 · see citing works

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Fatani, A., Dahou, A., Al‐qaness, M. A. A., Lu, S., & Elaziz, M. A. (2021) · Sensors · doi:10.3390/s22010140 · Cited by 182 · see citing works

Survey of Machine Learning based intrusion detection methods for Internet of Medical Things

Si-Ahmed, A., Al-Garadi, M. A., & Boustia, N. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110227 · Cited by 188 · see citing works

Deep Learning Approach for SDN-Enabled Intrusion Detection System in IoT Networks

Chaganti, R., Suliman, W., Ravi, V., & Dua, A. (2023) · Information · doi:10.3390/info14010041 · Cited by 174 · see citing works

DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU)

Rehman, S. U., Khaliq, M., Imtiaz, S. I., Rasool, A., Shafiq, M., Javed, A. R., Jalil, Z., & Bashir, A. K. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.01.022 · Cited by 168 · see citing works

Task Offloading in Fog Computing for Using Smart Ant Colony Optimization

Kishor, A., & Chakarbarty, C. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08714-7 · Cited by 174 · see citing works

A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis

Muneer, S., Farooq, U., Athar, A., Raza, M. A., Ghazal, T. M., & Sakib, S. (2024) · Journal of Engineering · doi:10.1155/2024/3909173 · Cited by 204 · see citing works

A Comprehensive Survey on Computer Forensics: State-of-the-Art, Tools, Techniques, Challenges, and Future Directions

Javed, A. R., Ahmed, W., Alazab, M., Jalil, Z., Kifayat, K., & Gadekallu, T. R. (2022) · IEEE Access · doi:10.1109/access.2022.3142508 · Cited by 160 · see citing works

A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks

Kumar, P., Gupta, G. P., & Tripathi, R. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02696-3 · Cited by 159 · see citing works

Fed-Inforce-Fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks

Khan, I. A., Razzak, I., Pi, D., Khan, N., Hussain, Y., Li, B., & Kousar, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102002 · Cited by 147 · see citing works

Towards Machine Learning Based Intrusion Detection in IoT Networks

Islam, N., Farhin, F., Sultana, I., Kaiser, M., Rahman, M. S., Mahmud, M., Hosen, A. S. M. S., & Cho, G. H. (2021) · Computers Materials & Continua · doi:10.32604/cmc.2021.018466 · Cited by 133 · see citing works

Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways

Nguyen, X.-H., Nguyen, X.-D., Huynh, H.-H., & Le, K.-H. (2022) · Sensors · doi:10.3390/s22020432 · Cited by 152 · see citing works

Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset

Abdalgawad, N. M., Sajun, A. R., Kaddoura, Y., Zualkernan, I. A., & Aloul, F. (2021) · IEEE Access · doi:10.1109/access.2021.3140015 · Cited by 143 · see citing works

Cross corpus multi-lingual speech emotion recognition using ensemble learning

Zehra, W., Javed, A. R., Jalil, Z., Khan, H. U., & Gadekallu, T. R. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00250-4 · Cited by 139 · see citing works

Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm

Dahou, A., Elaziz, M. A., Chelloug, S. A., Awadallah, M. A., Al‐Betar, M. A., Al‐qaness, M. A. A., & Forestiero, A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6473507 · Cited by 145 · see citing works

Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection

Mohakud, R., & Dash, R. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.05.012 · Cited by 136 · see citing works

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

Razib, M. A., Javeed, D., Khan, M. T., Alkanhel, R. I., & Muthanna, M. S. A. (2022) · IEEE Access · doi:10.1109/access.2022.3172304 · Cited by 140 · see citing works

XSRU-IoMT: Explainable simple recurrent units for threat detection in Internet of Medical Things networks

Khan, I. A., Moustafa, N., Razzak, I., Tanveer, M., Pi, D., Pan, Y., & Ali, B. S. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.010 · Cited by 131 · see citing works

Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making

Basile, L. J., Carbonara, N., Pellegrino, R., & Panniello, U. (2022) · Technovation · doi:10.1016/j.technovation.2022.102482 · Cited by 131 · see citing works

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Alkanhel, R. I., El‐kenawy, E. M., Abdelhamid, A. A., Ibrahim, A. F., Alohali, M. A., Abotaleb, M. S. A., & Khafaga, D. S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.033273 · Cited by 129 · see citing works

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed, S. S., Hassan, W. H., Latiff, L. A., & Ghabban, F. M. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.414 · Cited by 134 · see citing works

Analytical Study of Hybrid Techniques for Image Encryption and Decryption

Chowdhary, C. L., Patel, P. V., Kathrotia, K. J., Attique, M., Perumal, K., & Ijaz, M. F. (2020) · Sensors · doi:10.3390/s20185162 · Cited by 138 · see citing works

Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Messinis, S., Temenos, N., Protonotarios, N. Ε., Rallis, I., Kalogeras, D., & Doulamis, N. D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108036 · Cited by 132 · see citing works

Deep learning-driven methods for network-based intrusion detection systems: A systematic review

Chinnasamy, R., Subramanian, M., Easwaramoorthy, S. V., & Cho, J. (2025) · ICT Express · doi:10.1016/j.icte.2025.01.005 · Cited by 145 · see citing works

Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection

Ali, S., Rehman, S. U., Imran, A., Adeem, G., Iqbal, Z., & Kim, K. (2022) · Electronics · doi:10.3390/electronics11233934 · Cited by 118 · see citing works

Security and Privacy Management in Internet of Medical Things (IoMT): A Synthesis

Hireche, R., Mansouri, H., & Pathan, A. K. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2030033 · Cited by 117 · see citing works

Blockchain-Based Two-Stage Federated Learning With Non-IID Data in IoMT System

Lian, Z., Zeng, Q., Wang, W., Gadekallu, T. R., & Su, C. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3216802 · Cited by 117 · see citing works

A comprehensive and systematic literature review on intrusion detection systems in the internet of medical things: current status, challenges, and opportunities

Naghib, A., Gharehchopogh, F. S., & Zamanifar, A. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11101-w · Cited by 112 · see citing works

Intrusion detection approach for cloud and IoT environments using deep learning and Capuchin Search Algorithm

Elaziz, M. A., Al‐qaness, M. A. A., Dahou, A., Ibrahim, R. A., & El‐Latif, A. A. A. (2023) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103402 · Cited by 110 · see citing works

A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning

Kumaar, M. A., Samiayya, D., Vincent, P. M. D. R., Srinivasan, K., Chang, C., & Ganesh, H. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2021.824898 · Cited by 109 · see citing works

CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

Alabsi, B. A., Anbar, M., & Rihan, S. D. A. (2023) · Sensors · doi:10.3390/s23146507 · Cited by 107 · see citing works

Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers

Koç, K., Ekmekcioğlu, Ö., & Gürgün, A. P. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103896 · Cited by 105 · see citing works

Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

Smmarwar, S. K., Gupta, G. P., & Kumar, S. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100130 · Cited by 102 · see citing works

A novel permission-based Android malware detection system using feature selection based on linear regression

Şahi̇n, D. Ö., Kural, O. E., Akleylek, S., & Kılıç, E. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05875-1 · Cited by 101 · see citing works

Design of Anomaly-Based Intrusion Detection System Using Fog Computing for IoT Network

Kumar, P., Gupta, G. P., & Tripathi, R. (2021) · Automatic Control and Computer Sciences · doi:10.3103/s0146411621020085 · Cited by 100 · see citing works

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun, M. A., Memon, S. F., Eising, C., & Dhirani, L. L. (2023) · IEEE Access · doi:10.1109/access.2023.3346320 · Cited by 128 · see citing works

Deep Learning-Based Network Intrusion Detection System for Internet of Medical Things

Ravi, V., Pham, T. D., & Alazab, M. (2023) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2300021 · Cited by 98 · see citing works

Machine Learning and Deep Learning Methods for Intrusion Detection Systems in IoMT: A survey

Rbah, Y., Mahfoudi, M., Balboul, Y., Fattah, M. Y., Mazer, S., Elbekkali, M., & Bernoussi, B. (2022) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset52964.2022.9738218 · Cited by 97 · see citing works

Classification model for accuracy and intrusion detection using machine learning approach

Agarwal, A., Sharma, P., Alshehri, M., Mohamed, A. A., & Alfarraj, O. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.437 · Cited by 95 · see citing works

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

Nayak, J., Meher, S. K., Souri, A., Naik, B., & Vimal, S. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04453-z · Cited by 95 · see citing works

DNNBoT: Deep Neural Network-Based Botnet Detection and Classification

Verma, S., Sharma, N., Singh, A. R., Alharbi, A., Alosaimi, W. A., Alyami, H., Gupta, D., & Goyal, N. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.020938 · Cited by 94 · see citing works

Federated Learning for IoMT Applications: A Standardization and Benchmarking Framework of Intrusion Detection Systems

Alamleh, A., Albahri, O. S., Zaidan, A. A., Albahri, A. S., Alamoodi, A. H., Zaidan, B. B., Qahtan, S., Alsatar, H. A., Al-Samarraay, M. S., & Jasim, A. N. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3167256 · Cited by 93 · see citing works

Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions

Bharati, S., & Podder, P. (2022) · Security and Communication Networks · doi:10.1155/2022/8951961 · Cited by 101 · see citing works

Artificial Intelligence driven security model for Internet of Medical Things (IoMT)

Anitha, C., Komala, C. R., Vivekanand, C. V., Lalitha, S. D., Boopathi, S., & Revathi, R. (2023) · International Conference on Innovative Practices in Technology and Management (ICIPTM) · doi:10.1109/iciptm57143.2023.10117713 · Cited by 92 · see citing works

A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats

Alsalman, D. (2024) · IEEE Access · doi:10.1109/access.2024.3359033 · Cited by 90 · see citing works

GEMLIDS-MIOT: A Green Effective Machine Learning Intrusion Detection System based on Federated Learning for Medical IoT network security hardening

Ioannou, I. I., Nagaradjane, P., Angın, P., Balasubramanian, P., Kavitha, K. J., Murugan, P., & Vassiliou, V. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.02.023 · Cited by 89 · see citing works

Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data

Han, B., Jhaveri, R. H., Wang, H., Qiao, D., & Du, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3123936 · Cited by 88 · see citing works

A robust intelligent zero-day cyber-attack detection technique

Kumar, V., & Sinha, D. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00396-9 · Cited by 87 · see citing works

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

Alalhareth, M., & Hong, S. (2023) · Sensors · doi:10.3390/s23104971 · Cited by 85 · see citing works

A deep Reinforcement learning-based robust Intrusion Detection System for securing IoMT Healthcare Networks

Shaikh, J. A., Wang, C., Sima, M. W. U., Arshad, M., Owais, M., Hassan, D. S. M., Alkanhel, R. I., & Muthanna, M. S. A. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1524286 · Cited by 84 · see citing works

Scenario-based automated data preprocessing to predict severity of construction accidents

Koç, K., & Gürgün, A. P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104351 · Cited by 83 · see citing works

Empowering Healthcare With IoMT: Evolution, Machine Learning Integration, Security, and Interoperability Challenges

Pradyumna, G. R., Hegde, R. B., Bommegowda, K. B., Jan, T., & Naik, G. R. (2024) · IEEE Access · doi:10.1109/access.2024.3362239 · Cited by 82 · see citing works

An intelligent DDoS attack detection tree-based model using Gini index feature selection method

Bouke, M. A., Abdullah, A., ALshatebi, S. H., Abdullah, M. T., & Atigh, H. E. (2023) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2023.104823 · Cited by 85 · see citing works

A comprehensive survey on digital video forensics: Taxonomy, challenges, and future directions

Javed, A. R., Jalil, Z., Zehra, W., Gadekallu, T. R., Suh, D. Y., & Piran, M. J. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104456 · Cited by 78 · see citing works

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Shakeel, T., Habib, S., Boulila, W., Koubâa, A., Javed, A. R., Rizwan, M., Gadekallu, T. R., & Sufiyan, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00767-w · Cited by 77 · 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

A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection

Wagan, S. A., Koo, J., Siddiqui, I. F., Qureshi, N. M. F., Attique, M., & Shin, D. R. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.11.007 · Cited by 74 · see citing works

Attention-Based Multidimensional Deep Learning Approach for Cross-Architecture IoMT Malware Detection and Classification in Healthcare Cyber-Physical Systems

Ravi, V., Pham, T. D., & Alazab, M. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3198123 · Cited by 71 · see citing works

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System

Zubair, M., Ghubaish, A., Ünal, D., Al-Ali, A. K., Reimann, T., Alinier, G., Hammoudeh, M. A. A., & Qadir, J. (2022) · Sensors · doi:10.3390/s22218280 · Cited by 71 · see citing works

Machine learning enabled Industrial IoT Security: Challenges, Trends and Solutions

Ni, C., & Li, S. C. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100549 · Cited by 71 · see citing works

A Multi-View attention-based deep learning framework for malware detection in smart healthcare systems

Ravi, V., Alazab, M., Selvaganapathy, S., & Chaganti, R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.08.015 · Cited by 70 · see citing works

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection

Balhareth, G., & Ilyas, M. (2024) · Sensors · doi:10.3390/s24175712 · Cited by 76 · see citing works

A Novel Collaborative SRU Network With Dynamic Behaviour Aggregation, Reduced Communication Overhead and Explainable Features

Khan, I. A., Razzak, I., Pi, D., Zia, U., Kamal, S., & Hussain, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3352013 · Cited by 67 · see citing works

An Ensemble-Based Machine Learning-Envisioned Intrusion Detection in Industry 5.0-Driven Healthcare Applications

Wazid, M., Singh, J., Das, A. K., & Rodrigues, J. J. P. C. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3318850 · Cited by 66 · see citing works

Hybrid Optimized Deep Neural Network with Enhanced Conditional Random Field Based Intrusion Detection on Wireless Sensor Network

Karthic, S., & Kumar, S. M. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10892-9 · Cited by 65 · see citing works

Ensemble Model for Diagnostic Classification of Alzheimer’s Disease Based on Brain Anatomical Magnetic Resonance Imaging

Khan, Y. F., Kaushik, B., Chowdhary, C. L., & Srivastava, G. (2022) · Diagnostics · doi:10.3390/diagnostics12123193 · Cited by 65 · see citing works

A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things ( IoT )

Al-Haija, Q. S. A., & Droos, A. (2024) · Expert Systems · doi:10.1111/exsy.13726 · Cited by 78 · see citing works

Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing

Abodayeh, K., Raza, A., Rafiq, M. U., Arif, M. S., Naveed, M., Zeb, Z., Abbas, S. Z., Shahzadi, K., Sarwar, S., Naveed, Q., et al. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.018708 · Cited by 64 · see citing works

Deep learning-based optimization of energy utilization in IoT-enabled smart cities: A pathway to sustainable development

Aljohani, A. A. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.08.075 · Cited by 63 · see citing works

Intrusion detection systems using classical machine learning techniques vs integrated unsupervised feature learning and deep neural network

Rawat, S., Srinivasan, A., Ravi, V., & Ghosh, U. (2020) · Internet Technology Letters · doi:10.1002/itl2.232 · Cited by 61 · see citing works

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

Weber, S. B., Stein, S., Pilgermann, M., & Schräder, T. (2023) · IEEE Access · doi:10.1109/access.2023.3270225 · Cited by 65 · see citing works

BAT Algorithm With fuzzy C-Ordered Means (BAFCOM) Clustering Segmentation and Enhanced Capsule Networks (ECN) for Brain Cancer MRI Images Classification

Alhassan, A. M., & Zainon, W. M. N. W. (2020) · IEEE Access · doi:10.1109/access.2020.3035803 · Cited by 58 · see citing works

Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network

Yao, C., Yu, Y., Yin, K., & Yang, J. (2022) · IEEE Access · doi:10.1109/access.2022.3210189 · Cited by 57 · see citing works

Ambient intelligence approach: Internet of Things based decision performance analysis for intrusion detection

Ramana, T. V., Thirunavukkarasan, M., Mohammed, A. S., Devarajan, G. G., & Nagarajan, S. M. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.007 · Cited by 56 · see citing works

A Review on Cyber Crimes on the Internet of Things

Kagita, M. K., Thilakarathne, N. N., Gadekallu, T. R., Maddikunta, P. K. R., & Singh, S. (2021) · Signals and communication technology · doi:10.1007/978-981-16-6186-0_4 · Cited by 56 · see citing works

A Hybrid Approach for Network Intrusion Detection

Mehmood, M., Javed, T., Nebhen, J., Abbas, S., Abid, R., Bojja, G. R., & Rizwan, M. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.019127 · Cited by 56 · see citing works

Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards

Ekmekcioğlu, Ö., & Koç, K. (2022) · CATENA · doi:10.1016/j.catena.2022.106379 · Cited by 54 · see citing works

Multi-attention DeepCRNN: an efficient and explainable intrusion detection framework for Internet of Medical Things environments

Sharma, N., & Shambharkar, P. G. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02402-9 · Cited by 60 · see citing works

Network intrusion detection based on IE-DBN model

Jia, H., Liu, J., Zhang, M., He, X., & Sun, W. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.07.016 · Cited by 54 · see citing works

Network intrusion detection system: A survey on artificial intelligence‐based techniques

Habeeb, M. S., & Babu, T. R. (2022) · Expert Systems · doi:10.1111/exsy.13066 · Cited by 53 · see citing works

A Composite Approach of Intrusion Detection Systems: Hybrid RNN and Correlation-Based Feature Optimization

Gautam, S., Henry, A., Zuhair, M., Rashid, M. U., Javed, A. R., & Maddikunta, P. K. R. (2022) · Electronics · doi:10.3390/electronics11213529 · Cited by 53 · see citing works

Physical health monitoring in a scottish cohort of schizophrenia patients - the role of ECG and blood pressure monitoring

Bushe, C., O'Neil, J., Wood, C., Bradley, A., Farren, M., & Turner, M. (2007) · European Psychiatry · doi:10.1016/j.eurpsy.2007.01.337 · Cited by 1 · see citing works

An effective classification using enhanced elephant herding optimization with convolution neural network for intrusion detection in IoMT architecture

Anjelin, D. P., & Kumar, S. G. (2024) · Cluster Computing · doi:10.1007/s10586-024-04512-5 · Cited by 17 · see citing works

A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things

Chaganti, R., Azrour, M., Ravi, V., Vemprala, N., Dua, A., & Bhushan, B. (2022) · Sustainability · doi:10.3390/su141912828 · Cited by 134 · see citing works

Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms

Saheed, Y. K., & Arowolo, M. O. (2021) · IEEE Access · doi:10.1109/access.2021.3128837 · Cited by 246 · see citing works

HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things

Berguiga, A., Harchay, A., & Massaoudi, A. (2025) · IEEE Access · doi:10.1109/access.2025.3543127 · Cited by 63 · see citing works