{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T21:33:56Z","timestamp":1777930436774,"version":"3.51.4"},"reference-count":94,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National First-class Undergraduate Major (Network Security and Law enforcement) Construction Project","award":["LGZD202408"],"award-info":[{"award-number":["LGZD202408"]}]},{"name":"National First-class Undergraduate Major (Network Security and Law enforcement) Construction Project","award":["Su Jiao Yan Han [2022] No. 2"],"award-info":[{"award-number":["Su Jiao Yan Han [2022] No. 2"]}]},{"name":"Central University Basic Scientific Research Business Fee Special Fund Project","award":["LGZD202408"],"award-info":[{"award-number":["LGZD202408"]}]},{"name":"Central University Basic Scientific Research Business Fee Special Fund Project","award":["Su Jiao Yan Han [2022] No. 2"],"award-info":[{"award-number":["Su Jiao Yan Han [2022] No. 2"]}]},{"name":"\u201dPublic Security Technology\u201d, a key discipline in Jiangsu Province during the 14th Five Year Plan period","award":["LGZD202408"],"award-info":[{"award-number":["LGZD202408"]}]},{"name":"\u201dPublic Security Technology\u201d, a key discipline in Jiangsu Province during the 14th Five Year Plan period","award":["Su Jiao Yan Han [2022] No. 2"],"award-info":[{"award-number":["Su Jiao Yan Han [2022] No. 2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>With the advancement of deep learning (DL) technology, DL-based intrusion detection models have emerged as a focal point of research within the domain of cybersecurity. This paper provides an overview of the datasets frequently utilized in the research. This article presents an overview of the widely utilized datasets in the research, establishing a basis for future investigation and analysis. The text subsequently summarizes the prevalent data preprocessing methods and feature engineering techniques utilized in intrusion detection. Following this, it provides a review of seven deep learning-based intrusion detection models, namely, deep autoencoders, deep belief networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers. Each model is examined from various dimensions, highlighting their unique architectures and applications within the context of cybersecurity. Furthermore, this paper broadens its scope to include intrusion detection techniques facilitated by the following two large-scale predictive models: the BERT series and the GPT series. These models, leveraging the power of transformers and attention mechanisms, have demonstrated remarkable capabilities in understanding and processing sequential data. In light of these findings, this paper concludes with a prospective outlook on future research directions. Four key areas have been identified for further research. By addressing these issues and advancing research in the aforementioned areas, this paper envisions a future in which DL-based intrusion detection systems are not only more accurate and efficient but also better aligned with the dynamic and evolving landscape of cybersecurity threats.<\/jats:p>","DOI":"10.3390\/jimaging10100254","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T12:44:31Z","timestamp":1728909871000},"page":"254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Current Status and Challenges and Future Trends of Deep Learning-Based Intrusion Detection Models"],"prefix":"10.3390","volume":"10","author":[{"given":"Yuqiang","family":"Wu","sequence":"first","affiliation":[{"name":"College of Information and Technology, Nanjing Police University, Nanjing 210023, China"},{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bailin","family":"Zou","sequence":"additional","affiliation":[{"name":"College of Information and Technology, Nanjing Police University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.procs.2020.07.080","article-title":"Training Guidance with KDD Cup 1999 and NSL-KDD Datasets of ANIDINR: Anomaly-Based Network Intrusion Detection System","volume":"175","author":"Serinelli","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hindy, H., Atkinson, R., Tachtatzis, C., Colin, J., and Bellekens, X. (2020). Utilizing Deep Learning Techniques for Effective Zero-Day Attack Detection. Electronics, 9.","DOI":"10.3390\/electronics9101684"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1109\/JSYST.2020.2992966","article-title":"A Comprehensive Survey of Databases and Deep Learning Methods for Cybersecurity and Intrusion Detection Systems","volume":"15","author":"Gumusbas","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/COMST.2019.2922584","article-title":"Intrusion Detection Systems: A Cross-Domain Overview","volume":"21","author":"Tidjon","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Alrawashedeh, K., and Purdy, C. (2016, January 18\u201320). Toward an Online Anomaly Intrusion Detection System Based on Deep Learning. Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA.","DOI":"10.1109\/ICMLA.2016.0040"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1109\/TSMCC.2010.2048428","article-title":"Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods","volume":"40","author":"Tavallaee","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/978-3-030-95161-0_5","article-title":"Statistical and Signature Analysis Methods of Intrusion Detection","volume":"Volume 115","author":"Oliynykov","year":"2022","journal-title":"Information Security Technologies in the Decentralized Distributed Networks"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102767","DOI":"10.1016\/j.jnca.2020.102767","article-title":"Deep Learning Methods in Network Intrusion Detection: A Survey and an Objective Comparison","volume":"169","author":"Gamage","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_9","first-page":"267","article-title":"Network Intrusion Detection Based on Deep Learning Model Optimized with Rule-Based Hybrid Feature Selection","volume":"29","author":"Ayo","year":"2020","journal-title":"Inf. Secur. J."},{"key":"ref_10","first-page":"8","article-title":"Deep Learning Approach on Network Intrusion Detection System Using NSL-KDD Dataset","volume":"11","author":"Gurung","year":"2019","journal-title":"Int. J. Comput. Netw. Inf. Secur."},{"key":"ref_11","first-page":"96","article-title":"Overview of Network Intrusion Detection Technology","volume":"5","author":"Sai","year":"2020","journal-title":"J. Inf. Secur."},{"key":"ref_12","unstructured":"Stolfo, S., Fan, W., Lee, W., Prodromidis, A., and Chan, P. (2024, March 05). KDD Cup 1999 Data. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets\/kdd+cup+1999+data."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MC.2018.2888764","article-title":"KDD Cup 99 Datasets: A Perspective on the Role of Datasets in Network Intrusion Detection Research","volume":"52","author":"Siddique","year":"2019","journal-title":"Computer"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Thomas, R., and Pavithran, D. (2018, January 28\u201329). A Survey of Intrusion Detection Models Based on NSL-KDD Data Set. Proceedings of the 2018 Fifth HCT Information Technology Trends (ITT), Dubai, United Arab Emirates.","DOI":"10.1109\/CTIT.2018.8649498"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.ins.2019.10.069","article-title":"A Hybrid Deep Learning Model for Efficient Intrusion Detection in Big Data Environment","volume":"513","author":"Hassan","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_16","first-page":"177","article-title":"Towards a Reliable Intrusion Detection Benchmark Dataset","volume":"2018","author":"Sharafaldin","year":"2018","journal-title":"Softw. Netw."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Song, J., Takakura, H., and Okabe, Y. (2011, January 10\u201313). Statistical Analysis of Honeypot Data and Building of Kyoto 2006+ Dataset for NIDS Evaluation. Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, Salzburg, Austria.","DOI":"10.1145\/1978672.1978676"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Vaccari, I., Chiola, G., Aiello, M., Mongelli, M., and Cambiaso, E. (2020). MQTTset: A New Dataset for Machine Learning Techniques on MQTT. Sensors, 20.","DOI":"10.3390\/s20226578"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., and Ghorbani, A.A. (2023). CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment. Sensors, 23.","DOI":"10.20944\/preprints202305.0443.v1"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JIOT.2021.3085194","article-title":"ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets","volume":"9","author":"Booij","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_23","first-page":"1873","article-title":"Intrusion Detection Model Based on Deep Learning","volume":"28","author":"Lin","year":"2021","journal-title":"Control Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yan, Y., Qi, L., and Wang, J. (2020, January 7\u201311). A Network Intrusion Detection Method Based on Stacked Auto-Encoder and LSTM. Proceedings of the 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland.","DOI":"10.1109\/ICC40277.2020.9149384"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"195741","DOI":"10.1109\/ACCESS.2020.3034015","article-title":"A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"67542","DOI":"10.1109\/ACCESS.2020.2983568","article-title":"An Intrusion Detection Model with Hierarchical Attention Mechanism","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shahriar, M.H., Haque, N.I., and Rahman, M.A. (2020, January 13\u201317). G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System. Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC48688.2020.0-218"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103972","DOI":"10.1016\/j.compind.2023.103972","article-title":"Deep Attention SMOTE: Data Augmentation with a Learnable Interpolation Factor for Imbalanced Anomaly Detection of Gas Turbines","volume":"151","author":"Liu","year":"2023","journal-title":"Comput. Ind."},{"key":"ref_29","first-page":"8074516","article-title":"Prediction of Unbalanced Financial Risk Based on GRA-TOPSIS and SMOTE-CNN","volume":"2022","author":"Li","year":"2022","journal-title":"Sci. Prog."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7876","DOI":"10.1007\/s11227-023-05764-5","article-title":"ICS-IDS: Application of Big Data Analysis in AI-Based Intrusion Detection Systems to Identify Cyberattacks in ICS Networks","volume":"80","author":"Ali","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106167","DOI":"10.1016\/j.knosys.2020.106167","article-title":"Quantum-Inspired Ant Lion Optimized Hybrid K-Means for Cluster Analysis and Intrusion Detection","volume":"203","author":"Chen","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, L., Weng, E., Peng, C.J., Shuai, H.H., and Cheng, W.H. (2021, January 15\u201317). ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection. Proceedings of the 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Penghu, Taiwan.","DOI":"10.1109\/ICCE-TW52618.2021.9602909"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Farahnakian, F., and Heikkonen, J. (2018, January 11\u201314). A Deep Auto-Encoder Based Approach for Intrusion Detection System. Proceedings of the 2018 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon, Republic of Korea.","DOI":"10.23919\/ICACT.2018.8323688"},{"key":"ref_34","unstructured":"Farid, D.M., Harbi, N., and Rahman, M.Z. (2010). Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.procs.2016.06.047","article-title":"Random Forest Modeling for Network Intrusion Detection System","volume":"89","author":"Farnaaz","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_36","first-page":"102419","article-title":"Deep Learning for Cybersecurity Intrusion Detection: Approaches, Datasets, and Comparative Study","volume":"50","author":"Ferrag","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Patsakis, C., Casino, F., and Lykousas, N. (2024). Assessing LLMs in Malicious Code Deobfuscation of Real-World Malware Campaigns. arXiv.","DOI":"10.1016\/j.eswa.2024.124912"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","article-title":"A Deep Learning Approach to Network Intrusion Detection","volume":"2","author":"Shone","year":"2018","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"30373","DOI":"10.1109\/ACCESS.2019.2899721","article-title":"A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection","volume":"7","author":"Khan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"41238","DOI":"10.1109\/ACCESS.2018.2858277","article-title":"Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System","volume":"6","author":"Yan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Peng, W., Kong, X., and Peng, G. (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_42","doi-asserted-by":"crossref","unstructured":"Thaseen, I.S., and Kumar, C.A. (2014, January 27\u201329). Intrusion Detection Model Using Fusion of PCA and Optimized SVM. Proceedings of the 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India.","DOI":"10.1109\/IC3I.2014.7019692"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"52843","DOI":"10.1109\/ACCESS.2018.2869577","article-title":"Deep Learning Approach Combining Sparse Autoencoder with SVM for Network Intrusion Detection","volume":"6","author":"Habib","year":"2018","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"108346","DOI":"10.1109\/ACCESS.2020.3001350","article-title":"Anomaly-Based Intrusion Detection from Network Flow Features Using Variational Autoencoder","volume":"8","author":"Zavrak","year":"2020","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.comnet.2018.07.025","article-title":"An Evaluation of the Performance of Restricted Boltzmann Machines as a Model for Anomaly Network Intrusion Detection","volume":"144","author":"Aldwairi","year":"2018","journal-title":"Comput. Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103340","DOI":"10.1016\/j.pdpdt.2023.103340","article-title":"Comparison between Support Vector Machine (SVM) and Deep Belief Network (DBN) for Multi-Classification of Raman Spectroscopy for Cervical Diseases","volume":"42","author":"Wu","year":"2023","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/JAS.2020.1003099","article-title":"A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine","volume":"7","author":"Zhang","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, G., Zhang, C., and Zheng, L. (2017, January 21\u201324). Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network. Proceedings of the 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou, China.","DOI":"10.1109\/CSE-EUC.2017.119"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zheng, K., Wu, C., Niu, X., and Yang, Y. (2019). Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks. Appl. Sci., 9.","DOI":"10.3390\/app9020238"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"31711","DOI":"10.1109\/ACCESS.2019.2903723","article-title":"Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"16062","DOI":"10.1109\/ACCESS.2021.3051074","article-title":"Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Vigneswaran, R.K., Vinayakumar, R., and Soman, K.P. (2018, January 10\u201312). Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security. Proceedings of the 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Bengaluru, India.","DOI":"10.1109\/ICCCNT.2018.8494096"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ma, T., Wang, F., and Cheng, J. (2016). A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks. Sensors, 16.","DOI":"10.3390\/s16101701"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Khare, N., Devan, P., and Chowdhary, C.L. (2020). SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection. Electronics, 9.","DOI":"10.3390\/electronics9040692"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zheng, K., Wu, C., and Yang, Y. (2019). Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational Autoencoder and Deep Neural Network. Sensors, 19.","DOI":"10.3390\/s19112528"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Khan, R.U., Zhang, X., Alazab, M., and Kumar, R. (2019, January 8\u20139). An Improved Convolutional Neural Network Model for Intrusion Detection in Networks. Proceedings of the 2019 Cybersecurity and Cyberforensics Conference (CCC), Melbourne, VIC, Australia.","DOI":"10.1109\/CCC.2019.000-6"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"17265","DOI":"10.1007\/s00500-020-05017-0","article-title":"A Deep Learning Approach for Effective Intrusion Detection in Wireless Networks Using CNN","volume":"24","author":"Riyaz","year":"2020","journal-title":"Soft Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"50850","DOI":"10.1109\/ACCESS.2018.2868993","article-title":"A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks","volume":"6","author":"Wu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_59","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_60","first-page":"119","article-title":"Intrusion Detection Algorithm Based on Convolutional Neural Network and Three Branch Decision","volume":"58","author":"Wu","year":"2022","journal-title":"Comput. Eng. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/ACCESS.2017.2780250","article-title":"HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection","volume":"6","author":"Wang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional Recurrent Neural Networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"29575","DOI":"10.1109\/ACCESS.2020.2972627","article-title":"BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset","volume":"8","author":"Su","year":"2020","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mirza, A.H., and Cosan, S. (2018, January 2\u20135). Computer Network Intrusion Detection Using Sequential LSTM Neural Networks Autoencoders. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turke.","DOI":"10.1109\/SIU.2018.8404689"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Agarap, A.F. (2018, January 26\u201328). A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data. Proceedings of the 2018 10th International Conference on Machine Learning and Computing, Macau, China.","DOI":"10.1145\/3195106.3195117"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"48697","DOI":"10.1109\/ACCESS.2018.2867564","article-title":"An Intrusion Detection System Using a Deep Neural Network with Gated Recurrent Units","volume":"6","author":"Xu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Vinayakumar, R., Soman, K.P., and Poornachandran, P. (2017, January 13\u201316). Applying Convolutional Neural Network for Network Intrusion Detection. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126009"},{"key":"ref_69","first-page":"101322","article-title":"A Hybrid CNN+LSTM-Based Intrusion Detection System for Industrial IoT Networks","volume":"38","author":"Altunay","year":"2023","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Salem, M., Taheri, S., and Yuan, J.S. (2018, January 8\u201310). Anomaly Generation Using Generative Adversarial Networks in Host-Based Intrusion Detection. Proceedings of the IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON.2018.8796769"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Li, D., Kotani, D., and Okabe, Y. (2020, January 13\u201317). Improving Attack Detection Performance in NIDS Using GAN. Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC48688.2020.0-162"},{"key":"ref_72","first-page":"9947059","article-title":"A GAN and Feature Selection-Based Oversampling Technique for Intrusion Detection","volume":"2021","author":"Liu","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_73","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., and Kaiser, \u0141. (2017). Attention Is All You Need. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"108328","DOI":"10.1016\/j.comnet.2021.108328","article-title":"Intrusion Detection for Capsule Networks Based on Dual Routing Mechanism","volume":"197","author":"Yin","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_75","first-page":"80","article-title":"Intrusion Detection System Based on Dual Attention","volume":"22","author":"Liu","year":"2022","journal-title":"NetInfo Secur."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"19463","DOI":"10.1007\/s11042-022-14121-2","article-title":"A CNN-Transformer Hybrid Approach for an Intrusion Detection System in Advanced Metering Infrastructure","volume":"82","author":"Yao","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"103171","DOI":"10.1016\/j.cose.2023.103171","article-title":"Network Intrusion Detection Based on N-Gram Frequency and Time-Aware Transformer","volume":"128","author":"Han","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"109982","DOI":"10.1016\/j.comnet.2023.109982","article-title":"Res-TranBiLSTM: An Intelligent Approach for Intrusion Detection in the Internet of Things","volume":"235","author":"Wang","year":"2023","journal-title":"Comput. Netw."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13677-023-00574-9","article-title":"A Transformer-Based Network Intrusion Detection Approach for Cloud Security","volume":"13","author":"Long","year":"2024","journal-title":"J. Cloud Comput."},{"key":"ref_80","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Nguyen, G.L., and Watabe, K. (June, January 28). A Method for Network Intrusion Detection Using Flow Sequence and BERT Framework. Proceedings of the ICC 2023\u2014IEEE International Conference on Communications, Rome, Italy.","DOI":"10.1109\/ICC45041.2023.10279335"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1007\/s11845-023-03377-8","article-title":"GPT-4: A New Era of Artificial Intelligence in Medicine","volume":"192","author":"Waisberg","year":"2023","journal-title":"Ir. J. Med. Sci."},{"key":"ref_83","unstructured":"Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., and Fan, A. (2024). The LLaMA 3 Herd of Models. arXiv."},{"key":"ref_84","unstructured":"Houssel, P.R., Singh, P., Layeghy, S., and Portmann, M. (2024). Towards Explainable Network Intrusion Detection Using Large Language Models. arXiv."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"107936","DOI":"10.1016\/j.patcog.2021.107936","article-title":"A Survey on Heterogeneous Network Representation Learning","volume":"116","author":"Xie","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"122045","DOI":"10.1016\/j.eswa.2023.122045","article-title":"A Lightweight IoT Intrusion Detection Model Based on Improved BERT-of-Theseus","volume":"238","author":"Wang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"104128","DOI":"10.1016\/j.cose.2024.104128","article-title":"Adaptive Edge Security Framework for Dynamic IoT Security Policies in Diverse Environments","volume":"148","author":"Halgamuge","year":"2025","journal-title":"Comput. Secur."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Song, X., Chen, Q., Wang, S., and Song, T. (2024). Cross-Domain Resources Optimization for Hybrid Edge Computing Networks: Federated DRL Approach. Digit. Commun. Netw.","DOI":"10.1016\/j.dcan.2024.03.006"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"128511","DOI":"10.1016\/j.neucom.2024.128511","article-title":"Review of Neural Network Model Acceleration Techniques Based on FPGA Platforms","volume":"610","author":"Liu","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"806","DOI":"10.26599\/TST.2023.9010058","article-title":"Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization","volume":"29","author":"Zeng","year":"2024","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"103980","DOI":"10.1016\/j.jnca.2024.103980","article-title":"A Lightweight SEL for Attack Detection in IoT\/IIoT Networks","volume":"230","author":"Abdulkareem","year":"2024","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Kaur, A. (2024). Intrusion Detection Approach for Industrial Internet of Things Traffic Using Deep Recurrent Reinforcement Learning Assisted Federated Learning. IEEE Trans. Artif. Intell.","DOI":"10.1109\/TAI.2024.3443787"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"105439","DOI":"10.1016\/j.scs.2024.105439","article-title":"Smart Infrastructure Design: Machine Learning Solutions for Securing Modern Cities","volume":"107","author":"Wei","year":"2024","journal-title":"Sustain. Cities Soc."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"100443","DOI":"10.1016\/j.eij.2024.100443","article-title":"Fortifying Home IoT Security: A Framework for Comprehensive Examination of Vulnerabilities and Intrusion Detection Strategies for Smart Cities","volume":"25","author":"Bhardwaj","year":"2024","journal-title":"Egypt. Inform. J."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/10\/254\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:13:13Z","timestamp":1760112793000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/10\/254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,14]]},"references-count":94,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["jimaging10100254"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10100254","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,14]]}}}