{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T20:10:06Z","timestamp":1755893406218,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,8]]},"DOI":"10.1145\/3654446.3654452","type":"proceedings-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T18:00:57Z","timestamp":1714759257000},"page":"29-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Review of Intrusion Detection Research Based on Deep Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4366-4643","authenticated-orcid":false,"given":"Miaolei","family":"Deng","sequence":"first","affiliation":[{"name":"School of Information Science &Engineering, Henan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7531-875X","authenticated-orcid":false,"given":"Yupei","family":"Kan","sequence":"additional","affiliation":[{"name":"School of Information Science &Engineering, Henan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1955-8284","authenticated-orcid":false,"given":"Haihang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science &Engineering, Henan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7838-5012","authenticated-orcid":false,"given":"Chuanchuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Science &Engineering, Henan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Liming Fang Yang Li \"A Practical Model Based on Anomaly Detection for Protecting Medical IoT Control Services Against External Attacks \" IEEE Transactions on Industrial Informatics 4260-4269. 2021.","DOI":"10.1109\/TII.2020.3011444"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Tavallaee Mahbod . \"A detailed analysis of the KDD CUP 99 data set \" IEEE symposium on computational intelligence for security and defense applications 1-6. 2009.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"D. D. Proti\u0107 \"Review of KDD Cup \u201899 NSL-KDD and Kyoto 2006+ datasets \" Vojnotehni\u010dki glasnik\/Military Technical Courier 580-596. 2018.","DOI":"10.5937\/vojtehg66-16670"},{"volume-title":"a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),\" 2015 military communications and information systems conference (MilCIS), 1-6","year":"2015","key":"e_1_3_2_1_4_1","unstructured":"Moustafa, Nour, and Jill Slay. \"UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),\" 2015 military communications and information systems conference (MilCIS), 1-6. 2015."},{"key":"e_1_3_2_1_5_1","volume-title":"Toward generating a new intrusion detection dataset and intrusion traffic characterization,\" ICISSp, 108-116","author":"Sharafaldin I.","year":"2018","unstructured":"I. Sharafaldin, A. H. Lashkari and A. A. Ghorbani, \"Toward generating a new intrusion detection dataset and intrusion traffic characterization,\" ICISSp, 108-116. 2018."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"S. M. Kasongo \"A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework \" Computer Communications 113-125. 2023.","DOI":"10.1016\/j.comcom.2022.12.010"},{"key":"e_1_3_2_1_7_1","volume-title":"A two-stage intrusion detection system with auto-encoder and LSTMs,\" Applied Soft Computing, 108768","author":"Mushtaq E.","year":"2022","unstructured":"E. Mushtaq, A. Zameer, M. Umer and A. A. Abbasi, \"A two-stage intrusion detection system with auto-encoder and LSTMs,\" Applied Soft Computing, 108768. 2022."},{"key":"e_1_3_2_1_8_1","volume-title":"Knacks of a hybrid anomaly detection model using deep auto-encoder driven gated recurrent unit,\" Computer Networks, 109681","author":"Mushtaq E.","year":"2023","unstructured":"E. Mushtaq, A. Zameer and R. Nasir, \"Knacks of a hybrid anomaly detection model using deep auto-encoder driven gated recurrent unit,\" Computer Networks, 109681. 2023."},{"key":"e_1_3_2_1_9_1","volume-title":"Cloud intrusion detection method based on stacked contractive auto-encoder and support vector machine,\" IEEE transactions on cloud computing, 1634-1646","author":"Wang W.","year":"2020","unstructured":"W. Wang, X. Du, D. Shan, R. Qin and N. Wang, \"Cloud intrusion detection method based on stacked contractive auto-encoder and support vector machine,\" IEEE transactions on cloud computing, 1634-1646. 2020."},{"key":"e_1_3_2_1_10_1","volume":"1000","author":"Liu C.","year":"2022","unstructured":"C. Liu, R. Antypenko, I. Sushko and O. Zakharchenko, \"Intrusion detection system after data augmentation schemes based on the VAE and CVAE,\" IEEE Transactions on Reliability, 1000-1010. 2022.","journal-title":"\"Intrusion detection system after data augmentation schemes based on the VAE and CVAE,\" IEEE Transactions on Reliability"},{"key":"e_1_3_2_1_11_1","volume-title":"Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation,\" PeerJ Computer Science, e327","author":"Vaiyapuri T.","year":"2020","unstructured":"T. Vaiyapuri and A. Binbusayyis, \"Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation,\" PeerJ Computer Science, e327. 2020."},{"key":"e_1_3_2_1_12_1","volume-title":"DDoS Attacks Detection in the IoT Using Deep Gaussian-Bernoulli Restricted Boltzmann Machine,\" Modern Applied Science, 1-12","author":"Coli G. O.","year":"2022","unstructured":"G. O. Coli, S. Aina, S. D. Okegbile, A. R. Lawal and A. I. Oluwaranti, \"DDoS Attacks Detection in the IoT Using Deep Gaussian-Bernoulli Restricted Boltzmann Machine,\" Modern Applied Science, 1-12. 2022."},{"key":"e_1_3_2_1_13_1","volume":"49","author":"Rosa G. H.","year":"2021","unstructured":"G. H. d. Rosa, M. Roder, D. F. Santos and K. A. Costa, \"Enhancing anomaly detection through restricted boltzmann machine features projection,\" International Journal of Information Technology, 49-57. 2021.","journal-title":"\"Enhancing anomaly detection through restricted boltzmann machine features projection,\" International Journal of Information Technology"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Hong Liang and Bin Han. \"Intrusion detection method based on constrained Boltzmann machine and delayed decision \" Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering 979-985. 2022.","DOI":"10.1145\/3573428.3573605"},{"key":"e_1_3_2_1_15_1","volume-title":"A real-time and ubiquitous network attack detection based on deep belief network and support vector machine,\" IEEE\/CAA Journal of Automatica Sinica, 790-799","author":"Zhang H.","year":"2020","unstructured":"H. Zhang, Y. Li, Z. Lv, A. K. Sangaiah and T. Huang, \"A real-time and ubiquitous network attack detection based on deep belief network and support vector machine,\" IEEE\/CAA Journal of Automatica Sinica, 790-799. 2020."},{"key":"e_1_3_2_1_16_1","volume-title":"An efficient network behavior anomaly detection using a hybrid DBN-LSTM network,\" computers & security, 102600","author":"Chen A.","year":"2022","unstructured":"A. Chen, Y. Fu, X. Zheng and G. Lu, \"An efficient network behavior anomaly detection using a hybrid DBN-LSTM network,\" computers & security, 102600. 2022."},{"key":"e_1_3_2_1_17_1","volume-title":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS),1-6.","author":"Scalable Network Intrusion Detection A","year":"2023","unstructured":"Kanumalli, Satya Sandeep, \"A Scalable Network Intrusion Detection System using Bi-LSTM and CNN,\" 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS),1-6. 2023."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Y. Chen Q. Lin W. Wei J. Ji K.-C. Wong and C. A. C. Coello \"Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing \" Knowledge-Based Systems 108505. 2022.","DOI":"10.1016\/j.knosys.2022.108505"},{"key":"e_1_3_2_1_19_1","volume-title":"CANET: A hierarchical CNN-Attention model for Network Intrusion Detection,\" Computer Communications, 170-181","author":"Ren K.","year":"2023","unstructured":"K. Ren, S. Yuan, C. Zhang, Y. Shi and Z. Huang, \"CANET: A hierarchical CNN-Attention model for Network Intrusion Detection,\" Computer Communications, 170-181. 2023."},{"key":"e_1_3_2_1_20_1","volume-title":"An enhanced ai-based network intrusion detection system using generative adversarial networks,\" IEEE internet of things journal, 2330-2345","author":"Park C.","year":"2022","unstructured":"C. Park, J. Lee, Y. Kim, J.-G. Park, H. Kim and D. Hong, \"An enhanced ai-based network intrusion detection system using generative adversarial networks,\" IEEE internet of things journal, 2330-2345. 2022."},{"key":"e_1_3_2_1_21_1","volume-title":"A data balancing approach based on generative adversarial network,\" Future Generation Computer Systems, 768-776","author":"Yuan L.","year":"2023","unstructured":"L. Yuan, S. Yu, Z. Yang, M. Duan and K. Li, \"A data balancing approach based on generative adversarial network,\" Future Generation Computer Systems, 768-776. 2023."},{"volume-title":"A graph neural network based intrusion detection system for iot,\" NOMS 2022-2022 IEEE\/IFIP Network Operations and Management Symposium, 1-9","year":"2022","key":"e_1_3_2_1_22_1","unstructured":"Lo, Wai Weng, \"E-graphsage: A graph neural network based intrusion detection system for iot,\" NOMS 2022-2022 IEEE\/IFIP Network Operations and Management Symposium, 1-9. 2022."},{"key":"e_1_3_2_1_23_1","volume-title":"E-minBatch GraphSAGE: An Industrial Internet Attack Detection Model,\" Security and Communication Networks","author":"Lan J.","year":"2022","unstructured":"J. Lan, J. Z. Lu, G. G. Wan, Y. Y. Wang, C. Y. Huang, S. B. Zhang, Y. Y. Huang and J. N. Ma, \"E-minBatch GraphSAGE: An Industrial Internet Attack Detection Model,\" Security and Communication Networks, 2022."},{"key":"e_1_3_2_1_24_1","volume-title":"Graph-based solutions with residuals for intrusion detection: The modified e-graphsage and e-resgat algorithms,\" arXiv preprint arXiv:2111.13597","author":"Chang L.","year":"2021","unstructured":"L. Chang and P. Branco, \"Graph-based solutions with residuals for intrusion detection: The modified e-graphsage and e-resgat algorithms,\" arXiv preprint arXiv:2111.13597, 2021."},{"key":"e_1_3_2_1_25_1","volume-title":"TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems,\" IEEE Access, 165130-165150","author":"Alsaedi A.","year":"2020","unstructured":"A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood and A. Anwar, \"TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems,\" IEEE Access, 165130-165150. 2020."},{"key":"e_1_3_2_1_26_1","volume-title":"Anomal-E: A self-supervised network intrusion detection system based on graph neural networks,\" Knowledge-Based Systems, 110030","author":"Caville E.","year":"2022","unstructured":"E. Caville, W. W. Lo, S. Layeghy and M. Portmann, \"Anomal-E: A self-supervised network intrusion detection system based on graph neural networks,\" Knowledge-Based Systems, 110030. 2022."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"I. Sharafaldin A. H. Lashkari and A. A. J. I. Ghorbani \"Toward generating a new intrusion detection dataset and intrusion traffic characterization \" 108-116. 2018.","DOI":"10.5220\/0006639801080116"},{"key":"e_1_3_2_1_28_1","volume-title":"Empirical evaluation of threats and a public dataset,\" 184-208","author":"Kolias C.","year":"2015","unstructured":"C. Kolias, G. Kambourakis, A. Stavrou, S. J. I. C. S. Gritzalis and Tutorials, \"Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset,\" 184-208. 2015."},{"volume-title":"CRITIS 2016","year":"2016","key":"e_1_3_2_1_29_1","unstructured":"Goh, Jonathan, \"A dataset to support research in the design of secure water treatment systems.\" Critical Information Infrastructures Security: 11th International Conference, CRITIS 2016, Paris, France, October 10\u201312, 2016, Revised Selected Papers 11. 88-99. 2017."},{"key":"e_1_3_2_1_30_1","volume-title":"Bot-iot dataset,\" 779-796","author":"Koroniotis N.","year":"2019","unstructured":"N. Koroniotis, N. Moustafa, E. Sitnikova and B. J. F. G. C. S. Turnbull, \"Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,\" 779-796. 2019."},{"key":"e_1_3_2_1_31_1","volume-title":"Moustafa and Society, \"A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets,\" 102994","author":"N. J. S.","year":"2021","unstructured":"N. J. S. C. Moustafa and Society, \"A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets,\" 102994. 2021."},{"key":"e_1_3_2_1_32_1","volume-title":"n. Portmann and applications, \"Towards a standard feature set for network intrusion detection system datasets,\" 1-14","author":"Sarhan M.","year":"2022","unstructured":"M. Sarhan, S. Layeghy, M. J. M. n. Portmann and applications, \"Towards a standard feature set for network intrusion detection system datasets,\" 1-14. 2022."},{"key":"e_1_3_2_1_33_1","volume-title":"CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment","author":"Neto E. C. P.","year":"2023","unstructured":"E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu and A. A. Ghorbani, \"CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment,\". 2023."},{"key":"e_1_3_2_1_34_1","volume-title":"Documentation for the isot cloud intrusion detection benchmark dataset (isot-cid),\" University of Victoria","author":"Aldribi A.","year":"2020","unstructured":"A. Aldribi, I. Traore, P. G. Quinan and O. Nwamuo, \"Documentation for the isot cloud intrusion detection benchmark dataset (isot-cid),\" University of Victoria, 2020."},{"key":"e_1_3_2_1_35_1","volume-title":"A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions,\" Electronics, 1177","author":"Asharf J.","year":"2020","unstructured":"J. Asharf, N. Moustafa, H. Khurshid, E. Debie, W. Haider and A. Wahab, \"A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions,\" Electronics, 1177. 2020."}],"event":{"name":"SPCNC 2023: The 2nd International Conference on Signal Processing, Computer Networks and Communications","acronym":"SPCNC 2023","location":"Xiamen China"},"container-title":["Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3654446.3654452","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3654446.3654452","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:36:36Z","timestamp":1755891396000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3654446.3654452"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,8]]},"references-count":35,"alternative-id":["10.1145\/3654446.3654452","10.1145\/3654446"],"URL":"https:\/\/doi.org\/10.1145\/3654446.3654452","relation":{},"subject":[],"published":{"date-parts":[[2023,12,8]]},"assertion":[{"value":"2024-05-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}