{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T02:05:17Z","timestamp":1781661917890,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China National Natural Science Foundation","award":["61572034"],"award-info":[{"award-number":["61572034"]}]},{"name":"China National Natural Science Foundation","award":["202104d07020010"],"award-info":[{"award-number":["202104d07020010"]}]},{"name":"Project of Key Research and Development Program of Anhui Province","award":["61572034"],"award-info":[{"award-number":["61572034"]}]},{"name":"Project of Key Research and Development Program of Anhui Province","award":["202104d07020010"],"award-info":[{"award-number":["202104d07020010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>A large amount of sensitive information is generated in today\u2019s evolving network environment. Some hackers utilize low-frequency attacks to steal sensitive information from users. This generates minority attack samples in real network traffic. As a result, the data distribution in real network traffic is asymmetric, with a large number of normal traffic and a rare number of attack traffic. To address the data imbalance problem, intrusion detection systems mainly rely on machine-learning-based methods to detect minority attacks. Although this approach can detect minority attacks, the performance is not satisfactory. To solve the above-mentioned problems, this paper proposes a novel high-performance multimodal deep learning method. The method is based on deep learning. It captures the features of minority class attacks based on generative adversarial networks, which in turn generate high-quality minority class sample attacks. Meanwhile, it uses the designed multimodal deep learning model to learn the features of minority attacks. The integrated classifier then utilizes the learned features for multi-class classification. This approach achieves 99.55% and 99.95% F-measure, 99.56% and 99.96% accuracy on the CICIDS2017 dataset and the NSL-KDD dataset, respectively, with the highest false positives at only 3.4%. This exceeds the performance of current state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/sym16010042","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T09:35:21Z","timestamp":1703756121000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A High-Performance Multimodal Deep Learning Model for Detecting Minority Class Sample Attacks"],"prefix":"10.3390","volume":"16","author":[{"given":"Li","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuquan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuefeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1016\/j.chb.2009.05.009","article-title":"A nexus of Cyber-Geography and Cyber-Psychology: Topos\/\u201cNotopia\u201d and identity in hacking","volume":"25","author":"Papadimitriou","year":"2009","journal-title":"Comput. Hum. Behav."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/COMST.2018.2847722","article-title":"A detailed investigation and analysis of using machine learning techniques for intrusion detection","volume":"21","author":"Mishra","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101574","DOI":"10.1109\/ACCESS.2021.3097247","article-title":"Deep learning-based intrusion detection systems: A systematic review","volume":"9","author":"Lansky","year":"2021","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"69979","DOI":"10.1109\/ACCESS.2020.2987364","article-title":"Using cost-sensitive learning and feature selection algorithms to improve the performance of imbalanced classification","volume":"8","author":"Feng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107315","DOI":"10.1016\/j.comnet.2020.107315","article-title":"An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset","volume":"177","author":"Zhang","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chuang, P.-J., and Wu, D.-Y. (2019, January 18\u201320). Applying deep learning to balancing network intrusion detection datasets. Proceedings of the 2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT), Jinan, China.","DOI":"10.1109\/ICAIT.2019.8935927"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s42452-020-2414-z","article-title":"A deep learning-based multi-agent system for intrusion detection","volume":"2","author":"Louati","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102177","DOI":"10.1016\/j.cose.2021.102177","article-title":"Intrusion detection methods based on integrated deep learning model","volume":"103","author":"Wang","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1587\/transinf.2016ICP0018","article-title":"HFSTE: Hybrid feature selections and tree-based classifiers ensemble for intrusion detection system","volume":"100","author":"Tama","year":"2017","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Peng, W., Kong, X., Peng, G., Li, X., and Wang, Z. (2019, January 5\u20137). Network intrusion detection based on deep learning. Proceedings of the 2019 International Conference on Communications, Information System and Computer Engineering (CISCE), Haikou, China.","DOI":"10.1109\/CISCE.2019.00102"},{"key":"ref_11","unstructured":"Salama, M.A., Eid, H.F., Ramadan, R.A., Darwish, A., and Hassanien, A.E. (2011). Soft Computing in Industrial Applications, Springer."},{"key":"ref_12","first-page":"91","article-title":"A hybrid approach for network intrusion detection","volume":"70","author":"Mehmood","year":"2022","journal-title":"CMC-Comput. Mater. Contin"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Savanovi\u0107, N., Toskovic, A., Petrovic, A., Zivkovic, M., Dama\u0161evi\u010dius, R., Jovanovic, L., Bacanin, N., and Nikolic, B. (2023). Intrusion Detection in Healthcare 4.0 Internet of Things Systems via Metaheuristics Optimized Machine Learning. Sustainability, 15.","DOI":"10.3390\/su151612563"},{"key":"ref_14","first-page":"102312","article-title":"A novel metaheuristics with deep learning enabled intrusion detection system for secured smart environment","volume":"52","author":"Malibari","year":"2022","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Saif, S., Das, P., Biswas, S., Khari, M., and Shanmuganathan, V. (2022). HIIDS: Hybrid intelligent intrusion detection system empowered with machine learning and metaheuristic algorithms for application in IoT based healthcare. Microprocess. Microsyst., 104622.","DOI":"10.1016\/j.micpro.2022.104622"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"117936","DOI":"10.1016\/j.eswa.2022.117936","article-title":"Generating realistic cyber data for training and evaluating machine learning classifiers for network intrusion detection systems","volume":"207","author":"Bastian","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.inffus.2022.09.026","article-title":"Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System","volume":"90","author":"Thakkar","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/OJCS.2021.3050917","article-title":"A novel intrusion detection model for detecting known and innovative cyberattacks using convolutional neural network","volume":"2","author":"Ho","year":"2021","journal-title":"IEEE Open J. Comput. Soc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.future.2021.04.017","article-title":"GAN augmentation to deal with imbalance in imaging-based intrusion detection","volume":"123","author":"Andresini","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9438","DOI":"10.1007\/s11227-021-04285-3","article-title":"FSO-LSTM IDS: Hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks","volume":"78","author":"Alqahtani","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","article-title":"A deep learning approach for intrusion detection using recurrent neural networks","volume":"5","author":"Yin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107894","DOI":"10.1016\/j.knosys.2021.107894","article-title":"A bio-inspired hybrid deep learning model for network intrusion detection","volume":"238","author":"Moizuddin","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1016\/j.procs.2022.12.339","article-title":"Integrated Security Information and Event Management (SIEM) with Intrusion Detection System (IDS) for Live Analysis based on Machine Learning","volume":"217","author":"Muhammad","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102130","DOI":"10.1016\/j.datak.2022.102130","article-title":"Convolutional neural network-based high-precision and speed detection system on CIDDS-001","volume":"144","author":"Daoud","year":"2023","journal-title":"Data Knowl. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nayyar, S., Arora, S., and Singh, M. (2020, January 28\u201330). Recurrent neural network based intrusion detection system. Proceedings of the 2020 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP48568.2020.9182099"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.comcom.2022.12.010","article-title":"A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework","volume":"199","author":"Kasongo","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"118476","DOI":"10.1016\/j.eswa.2022.118476","article-title":"Remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features","volume":"210","author":"Pingale","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104695","DOI":"10.1109\/ACCESS.2021.3100087","article-title":"Network anomaly detection using memory-augmented deep autoencoder","volume":"9","author":"Min","year":"2021","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"22351","DOI":"10.1109\/ACCESS.2021.3056614","article-title":"Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset","volume":"9","author":"Maseer","year":"2021","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1109\/TIFS.2021.3050605","article-title":"Random partitioning forest for point-wise and collective anomaly detection\u2014Application to network intrusion detection","volume":"16","author":"Marteau","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13241","DOI":"10.1007\/s11227-023-05197-0","article-title":"EIDM: Deep learning model for IoT intrusion detection systems","volume":"79","author":"Elnakib","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1007\/s13369-021-06086-5","article-title":"A New Ensemble-Based Intrusion Detection System for Internet of Things","volume":"47","author":"Abbas","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102151","DOI":"10.1016\/j.cose.2020.102151","article-title":"RNNIDS: Enhancing network intrusion detection systems through deep learning","volume":"102","author":"Sohi","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102289","DOI":"10.1016\/j.cose.2021.102289","article-title":"A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM","volume":"106","author":"Liu","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102158","DOI":"10.1016\/j.cose.2020.102158","article-title":"An effective intrusion detection approach using SVM with na\u00efve Bayes feature embedding","volume":"103","author":"Gu","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Khan, M.A. (2021). HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system. Processes, 9.","DOI":"10.3390\/pr9050834"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"108076","DOI":"10.1016\/j.comnet.2021.108076","article-title":"LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system","volume":"192","author":"Gupta","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4951","DOI":"10.1109\/TNSM.2023.3260039","article-title":"Unsupervised GAN-Based Intrusion Detection System Using Temporal Convolutional Networks and Self-Attention","volume":"20","author":"Naili","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103054","DOI":"10.1016\/j.cose.2022.103054","article-title":"Synthetic attack data generation model applying generative adversarial network for intrusion detection","volume":"125","author":"Kumar","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.future.2022.12.024","article-title":"A data balancing approach based on generative adversarial network","volume":"141","author":"Yuan","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Babu, K.S., and Rao, Y.N. (2023). MCGAN: Modified Conditional Generative Adversarial Network (MCGAN) for Class Imbalance Problems in Network Intrusion Detection System. Appl. Sci., 13.","DOI":"10.3390\/app13042576"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9469","DOI":"10.1109\/ACCESS.2023.3240109","article-title":"Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication Networks","volume":"11","author":"Donkol","year":"2023","journal-title":"IEEE Access"},{"key":"ref_43","first-page":"101322","article-title":"A hybrid CNN+ LSTMbased intrusion detection system for industrial IoT networks","volume":"38","author":"Altunay","year":"2023","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Han, J., and Pak, W. (2023). Hierarchical LSTM-Based Network Intrusion Detection System Using Hybrid Classification. Appl. Sci., 13.","DOI":"10.3390\/app13053089"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"12175","DOI":"10.1007\/s00521-023-08376-5","article-title":"Flow-based intrusion detection on software-defined networks: A multivariate time series anomaly detection approach","volume":"35","author":"Zavrak","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rekha, G., and Tyagi, A.K. (2019, January 8\u20139). Necessary information to know to solve class imbalance problem: From a user\u2019s perspective. Proceedings of the ICRIC 2019: Recent Innovations in Computing, Jammu, India.","DOI":"10.1007\/978-3-030-29407-6_46"},{"key":"ref_47","unstructured":"Dubey, A.K., and Jain, V. (2019). Applications of Computing, Automation and Wireless Systems in Electrical Engineering, Springer."},{"key":"ref_48","first-page":"108","article-title":"Toward generating a new intrusion detection dataset and intrusion traffic characterization","volume":"1","author":"Sharafaldin","year":"2018","journal-title":"ICISSp"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"102975","DOI":"10.1016\/j.cose.2022.102975","article-title":"ExpSSOA-Deep maxout: Exponential Shuffled shepherd optimization based Deep maxout network for intrusion detection using big data in cloud computing framework","volume":"124","author":"Pandey","year":"2023","journal-title":"Comput. Secur."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/1\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:43:36Z","timestamp":1760132616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/1\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["sym16010042"],"URL":"https:\/\/doi.org\/10.3390\/sym16010042","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]}}}