{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T02:51:16Z","timestamp":1786157476937,"version":"3.56.0"},"reference-count":82,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T00:00:00Z","timestamp":1619222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.<\/jats:p>","DOI":"10.3390\/s21092987","type":"journal-article","created":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T02:12:57Z","timestamp":1619316777000},"page":"2987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4093-9349","authenticated-orcid":false,"given":"Raneem","family":"Qaddoura","sequence":"first","affiliation":[{"name":"Information Technology, Philadelphia University, Amman 19392, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0414-3570","authenticated-orcid":false,"given":"Ala\u2019","family":"M. Al-Zoubi","sequence":"additional","affiliation":[{"name":"School of Science, Technology and Engineering, University of Granada, 18010 Granada, Spain"},{"name":"King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossam","family":"Faris","sequence":"additional","affiliation":[{"name":"King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"},{"name":"School of Computing and Informatics, Al Hussein Technical University, Amman 11831, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4639-516X","authenticated-orcid":false,"given":"Iman","family":"Almomani","sequence":"additional","affiliation":[{"name":"King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"},{"name":"Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8202","DOI":"10.1109\/ACCESS.2020.2964280","article-title":"Multimedia Internet of Things: A comprehensive survey","volume":"8","author":"Nauman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qaddoura, R., and Manaseer, S. (November, January 31). Comparative Study for the Effect of CPU Speed in Fog Networks. Proceedings of the 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT), Amman, Jordan.","DOI":"10.1109\/ISIICT.2018.8613284"},{"key":"ref_3","first-page":"514","article-title":"Internet of Things: A Secure Cloud-based MANET Mobility Model","volume":"22","author":"Alam","year":"2020","journal-title":"Int. J. Netw. Secur."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.future.2019.09.016","article-title":"Agent-based Internet of Things: State-of-the-art and research challenges","volume":"102","author":"Savaglio","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"406","DOI":"10.15547\/tjs.2017.s.01.068","article-title":"The great impact of internet of things on business","volume":"15","author":"Angelova","year":"2017","journal-title":"Trakia J. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Thamilarasu, G., and Chawla, S. (2019). Towards deep-learning-driven intrusion detection for the internet of things. Sensors, 19.","DOI":"10.3390\/s19091977"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Williams, R., McMahon, E., Samtani, S., Patton, M., and Chen, H. (2017, January 22\u201324). Identifying vulnerabilities of consumer Internet of Things (IoT) devices: A scalable approach. Proceedings of the 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), Beijing, China.","DOI":"10.1109\/ISI.2017.8004904"},{"key":"ref_8","unstructured":"Darrell Etherington, K.C. (2021, April 21). Large DDoS Attacks Cause Outages at Twitter, Spotify, and Other Sites. Available online: https:\/\/techcrunch.com\/2016\/10\/21\/many-sites-including-twitter-and-spotify-suffering-outage\/."},{"key":"ref_9","unstructured":"Solon, O. (2016). Team of Hackers Take Remote Control of Tesla Model S from 12 Miles Away, The Guardian. Available online: https:\/\/www.theguardian.com\/technology\/2016\/sep\/20\/tesla-model-s-chinese-hack-remote-control-brakes."},{"key":"ref_10","first-page":"68","article-title":"Correlating Internet of Things","volume":"8","author":"Kumar","year":"2017","journal-title":"Int. J. Manag. (IJM)"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qiu, S., Wang, D., Xu, G., and Kumari, S. (2020). Practical and Provably Secure Three-Factor Authentication Protocol Based on Extended Chaotic-Maps for Mobile Lightweight Devices. IEEE Trans. Dependable Secur. Comput., 1.","DOI":"10.1109\/TDSC.2020.3022797"},{"key":"ref_12","unstructured":"Li, Z., Wang, D., and Morais, E. (2020). Quantum-Safe Round-Optimal Password Authentication for Mobile Devices. IEEE Trans. Dependable Secur. Comput., 1."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kasinathan, P., Costamagna, G., Khaleel, H., Pastrone, C., and Spirito, M.A. (2013, January 4\u20138). An IDS framework for internet of things empowered by 6LoWPAN. Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, Berlin, Germany.","DOI":"10.1145\/2508859.2512494"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Krimmling, J., and Peter, S. (2014, January 29\u201331). Integration and evaluation of intrusion detection for CoAP in smart city applications. Proceedings of the 2014 IEEE Conference on Communications and Network Security, San Francisco, CA, USA.","DOI":"10.1109\/CNS.2014.6997468"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1002\/dac.2356","article-title":"6LoWPAN: A study on QoS security threats and countermeasures using intrusion detection system approach","volume":"25","author":"Le","year":"2012","journal-title":"Int. J. Commun. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3335","DOI":"10.1007\/s00500-020-05439-w","article-title":"Evolutionary competitive swarm exploring optimal support vector machines and feature weighting","volume":"25","author":"Hassonah","year":"2021","journal-title":"Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107094","DOI":"10.1016\/j.compeleceng.2021.107094","article-title":"Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine","volume":"92","author":"Abbasi","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"22027","DOI":"10.1007\/s11042-020-09014-1","article-title":"Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer","volume":"79","author":"Qaddoura","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_19","unstructured":"Ala\u2019M, A.Z., Heidari, A.A., Habib, M., Faris, H., Aljarah, I., and Hassonah, M.A. (2020). Salp chain-based optimization of support vector machines and feature weighting for medical diagnostic information systems. Evolutionary Machine Learning Techniques, Springer."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s11869-018-0561-9","article-title":"Cycle reservoir with regular jumps for forecasting ozone concentrations: Two real cases from the east of Croatia","volume":"11","author":"Sheta","year":"2018","journal-title":"Air Qual. Atmos. Health."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","article-title":"Toward developing a systematic approach to generate benchmark datasets for intrusion detection","volume":"31","author":"Shiravi","year":"2012","journal-title":"Comput. Secur."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015, January 10\u201312). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, ACT, Australia.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., and Ghorbani, A.A. (2018, January 22\u201324). Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proceedings of the ICISSp, Funchal, Madeira, Portugal.","DOI":"10.5220\/0006639801080116"},{"key":"ref_24","unstructured":"Pahl, M.O., and Aubet, F.X. (2018, January 5\u20139). All eyes on you: Distributed Multi-Dimensional IoT microservice anomaly detection. Proceedings of the 2018 14th International Conference on Network and Service Management (CNSM), Rome, Italy."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","article-title":"Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset","volume":"100","author":"Koroniotis","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ullah, I., and Mahmoud, Q.H. (2020). A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks. Canadian Conference on Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-030-47358-7_52"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Damasevicius, R., Venckauskas, A., Grigaliunas, S., Toldinas, J., Morkevicius, N., Aleliunas, T., and Smuikys, P. (2020). LITNET-2020: An annotated real-world network flow dataset for network intrusion detection. Electronics, 9.","DOI":"10.3390\/electronics9050800"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yong, B., Wei, W., Li, K.C., Shen, J., Zhou, Q., Wozniak, M., Po\u0142ap, D., and Dama\u0161evi\u010dius, R. (2020). Ensemble machine learning approaches for webshell detection in Internet of things environments. Trans. Emerg. Telecommun. Technol., e4085.","DOI":"10.1002\/ett.4085"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sadiq, A.S., Faris, H., Ala\u2019M, A.Z., Mirjalili, S., and Ghafoor, K.Z. (2019). Fraud detection model based on multi-verse features extraction approach for smart city applications. Smart Cities Cybersecurity and Privacy, Elsevier.","DOI":"10.1016\/B978-0-12-815032-0.00017-2"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Khan, R., Khan, S.U., Zaheer, R., and Khan, S. (2012, January 17\u201319). Future internet: The internet of things architecture, possible applications and key challenges. Proceedings of the 2012 10th International Conference on Frontiers of Information Technology, Islamabad, Pakistan.","DOI":"10.1109\/FIT.2012.53"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Almomani, I., and Alromi, A. (2020). Integrating Software Engineering Processes in the Development of Efficient Intrusion Detection Systems in Wireless Sensor Networks. Sensors, 20.","DOI":"10.3390\/s20051375"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, T., Wendt, J.B., and Potkonjak, M. (2014, January 2\u20136). Security of IoT systems: Design challenges and opportunities. Proceedings of the 2014 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD), San Jose, CA, USA.","DOI":"10.1109\/ICCAD.2014.7001385"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s11277-011-0385-5","article-title":"Security Challenges in the IP-based Internet of Things","volume":"61","author":"Heer","year":"2011","journal-title":"Wirel. Pers. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.dcan.2017.04.003","article-title":"A roadmap for security challenges in the Internet of Things","volume":"4","author":"Sfar","year":"2018","journal-title":"Digit. Commun. Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.jnca.2015.12.006","article-title":"Intrusion response systems: Foundations, design, and challenges","volume":"62","author":"Inayat","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_37","unstructured":"Wu, Z., Xu, Z., and Wang, H. (2012). Whispers in the Hyper-space: High-speed Covert Channel Attacks in the Cloud. 21st USENIX Security Symposium (USENIX Security 12), USENIX Association."},{"key":"ref_38","first-page":"855","article-title":"A survey on security Issues and vulnerabilities on cloud computing","volume":"4","author":"Neela","year":"2013","journal-title":"Int. J. Comput. Sci. Eng. Technol."},{"key":"ref_39","unstructured":"Halfond, W.G., Viegas, J., and Orso, A. (2006, January 18). A classification of SQL-injection attacks and countermeasures. Proceedings of the IEEE International Symposium on Secure Software Engineering, Hong Kong, China."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Anwar, S., Mohamad Zain, J., Zolkipli, M.F., Inayat, Z., Khan, S., Anthony, B., and Chang, V. (2017). From intrusion detection to an intrusion response system: Fundamentals, requirements, and future directions. Algorithms, 10.","DOI":"10.3390\/a10020039"},{"key":"ref_41","unstructured":"Khan, A. (2016, January 10). Overview of Security in Internet of Things. Proceedings of the 3rd International Conference on Recent Trends in Engineering Science and Management, Bundi, Rajasthan, India."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Z.K., Cho, M.C.Y., and Shieh, S. (2015, January 14\u201317). Emerging security threats and countermeasures in IoT. Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security, Singapore.","DOI":"10.1145\/2714576.2737091"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/4731953","article-title":"WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks","volume":"2016","author":"Almomani","year":"2016","journal-title":"J. Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1109\/JIOT.2018.2847733","article-title":"The effect of iot new features on security and privacy: New threats, existing solutions, and challenges yet to be solved","volume":"6","author":"Zhou","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhao, K., and Ge, L. (2013, January 14\u201315). A survey on the internet of things security. Proceedings of the 2013 Ninth International Conference on Computational Intelligence and Security, Emeishan, China.","DOI":"10.1109\/CIS.2013.145"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1007\/s11277-019-06986-8","article-title":"Machine learning based intrusion detection systems for IoT applications","volume":"111","author":"Verma","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_47","unstructured":"Hindy, H., Bayne, E., Bures, M., Atkinson, R., Tachtatzis, C., and Bellekens, X. (2020). Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study. arXiv."},{"key":"ref_48","first-page":"977","article-title":"Efficient Denial of Service Attacks Detection in Wireless Sensor Networks","volume":"34","author":"Almomani","year":"2018","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Singh, T., and Kumar, N. (2020). Machine learning models for intrusion detection in IoT environment: A comprehensive review. Comput. Commun.","DOI":"10.1016\/j.comcom.2020.02.001"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"102631","DOI":"10.1016\/j.jnca.2020.102631","article-title":"Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments","volume":"161","author":"Li","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"102324","DOI":"10.1016\/j.scs.2020.102324","article-title":"Scalable Machine Learning-Based Intrusion Detection System for IoT-Enabled Smart Cities","volume":"61","author":"Rahman","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Amouri, A., Alaparthy, V.T., and Morgera, S.D. (2020). A Machine Learning Based Intrusion Detection System for Mobile Internet of Things. Sensors, 20.","DOI":"10.3390\/s20020461"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Qaddoura, R., Aljarah, I., Faris, H., and Almomani, I. (2021). A Classification Approach Based on Evolutionary Clustering and Its Application for Ransomware Detection. Evol. Data Clust. Algorithms Appl., 237\u2013248.","DOI":"10.1007\/978-981-33-4191-3_11"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","article-title":"A survey of network anomaly detection techniques","volume":"60","author":"Ahmed","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.ins.2014.09.025","article-title":"A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks","volume":"294","author":"Costa","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.future.2017.08.043","article-title":"Distributed attack detection scheme using deep learning approach for Internet of Things","volume":"82","author":"Diro","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liu, C., Yang, J., Chen, R., Zhang, Y., and Zeng, J. (2011, January 26\u201328). Research on immunity-based intrusion detection technology for the internet of things. Proceedings of the 2011 Seventh International Conference on Natural Computation, Shanghai, China.","DOI":"10.1109\/ICNC.2011.6022060"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s00521-017-3128-z","article-title":"An in-depth experimental study of anomaly detection using gradient boosted machine","volume":"31","author":"Tama","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Primartha, R., and Tama, B.A. (2017, January 1\u20132). Anomaly detection using random forest: A performance revisited. Proceedings of the 2017 International Conference on Data and Software Engineering (ICoDSE), Palembang, Indonesia.","DOI":"10.1109\/ICODSE.2017.8285847"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Maniriho, P., Niyigaba, E., Bizimana, Z., Twiringiyimana, V., Mahoro, L.J., and Ahmad, T. (2020, January 17\u201318). Anomaly-based Intrusion Detection Approach for IoT Networks Using Machine Learning. Proceedings of the 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), Surabaya, Indonesia.","DOI":"10.1109\/CENIM51130.2020.9297958"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"102177","DOI":"10.1016\/j.adhoc.2020.102177","article-title":"IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks","volume":"105","author":"Huang","year":"2020","journal-title":"Ad Hoc Netw."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Eberz, S., Rasmussen, K.B., Lenders, V., and Martinovic, I. (2017, January 2\u20136). Evaluating behavioral biometrics for continuous authentication: Challenges and metrics. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, Abu Dhabi, United Arab Emirates.","DOI":"10.1145\/3052973.3053032"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6882","DOI":"10.1109\/JIOT.2020.2970501","article-title":"Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices","volume":"7","author":"Eskandari","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Arrington, B., Barnett, L., Rufus, R., and Esterline, A. (2016, January 1\u20134). Behavioral modeling intrusion detection system (BMIDS) using internet of things (IoT) behavior-based anomaly detection via immunity-inspired algorithms. Proceedings of the 2016 25th International Conference on Computer Communication and Networks (ICCCN), Waikoloa, HI, USA.","DOI":"10.1109\/ICCCN.2016.7568495"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s13638-018-1128-z","article-title":"An intrusion detection method for internet of things based on suppressed fuzzy clustering","volume":"2018","author":"Liu","year":"2018","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary","volume":"61","author":"Garcia","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Qaddoura, R., Al-Zoubi, A., Almomani, I., and Faris, H. (2021). A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling. Appl. Sci., 11.","DOI":"10.3390\/app11073022"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chawla, N.V. (2009). Data mining for imbalanced datasets: An overview. Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/978-0-387-09823-4_45"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"106632","DOI":"10.1016\/j.asoc.2020.106632","article-title":"A Bayesian regularized feed-forward neural network model for conductivity prediction of PS\/MWCNT nanocomposite film coatings","volume":"96","author":"Demirbay","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.eswa.2018.08.038","article-title":"Feed-forward neural network training using sparse representation","volume":"116","author":"Yang","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Gers, F.A., Schmidhuber, J., and Cummins, F. (1999, January 7\u201310). Learning to forget: Continual prediction with LSTM. Proceedings of the 9th International Conference on Artificial Neural Networks: ICANN \u201999, Edinburgh, UK.","DOI":"10.1049\/cp:19991218"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to Forget: Continual Prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_78","first-page":"415","article-title":"Comparison of performance of Variants of Single-layer Perceptron Algorithms on Non-separable Datasets","volume":"8","author":"Parekh","year":"2000","journal-title":"Neural Parallel Sci. Comput."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.neucom.2016.12.088","article-title":"An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition","volume":"268","author":"Tsironi","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_80","first-page":"1","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Nogueira","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_81","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_82","unstructured":"Chollet, F. (2021, April 21). Keras. Available online: https:\/\/keras.io."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/2987\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:52:16Z","timestamp":1760161936000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/2987"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,24]]},"references-count":82,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21092987"],"URL":"https:\/\/doi.org\/10.3390\/s21092987","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,24]]}}}