{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:13:44Z","timestamp":1760058824378,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873309","23511100500","22510761000"],"award-info":[{"award-number":["61873309","23511100500","22510761000"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Project","award":["61873309","23511100500","22510761000"],"award-info":[{"award-number":["61873309","23511100500","22510761000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>This paper presents TCReC, an innovative model designed for reconstructing network traffic characteristics in the presence of packet loss. With the rapid expansion of wireless networks driven by edge computing, IoT, and 5G technologies, challenges such as transmission instability, channel competition, and environmental interference have led to significant packet loss rates, adversely impacting deep learning-based network traffic analysis tasks. To address this issue, TCReC leverages masked autoencoder techniques to reconstruct missing traffic features, ensuring reliable input for downstream tasks in edge computing scenarios. Experimental results demonstrate that TCReC maintains detection model accuracy within 10% of the original data, even under packet loss rates as high as 70%. For instance, on the ISCX-VPN-2016 dataset, TCReC achieves a Reconstruction Ability Index (RAI) of 94.02%, while on the CIC-IDS-2017 dataset, it achieves an RAI of 94.99% when combined with LSTM, significantly outperforming other methods such as Transformer, KNN, and RNN. Additionally, TCReC exhibits robustness across various packet loss scenarios, consistently delivering high-quality feature reconstruction for both attack traffic and common Internet application data. TCReC provides a robust solution for network traffic analysis in high-loss edge computing scenarios, offering practical value for real-world deployment.<\/jats:p>","DOI":"10.3390\/fi17050208","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T21:42:09Z","timestamp":1746481329000},"page":"208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Network Traffic Characteristics Reconstruction Method for Mitigating the Impact of Packet Loss in Edge Computing Scenarios"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1073-6444","authenticated-orcid":false,"given":"Jiawei","family":"Ye","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai 200433, China"}]},{"given":"Yanting","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai 200433, China"}]},{"given":"Aierpanjiang","family":"Simayi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1661-9401","authenticated-orcid":false,"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5706-7503","authenticated-orcid":false,"given":"Zhihui","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai 200433, China"}]},{"given":"Jie","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai 200433, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,5]]},"reference":[{"key":"ref_1","unstructured":"TechInsight (2025, March 22). Embedded WLAN (Wi-Fi) CE Device Global Market Analysis. Available online: https:\/\/www.techinsights.com\/blog\/embedded-wlan-wi-fi-ce-device-global-market-analysis."},{"key":"ref_2","unstructured":"Satyajit Sinha (2025, March 20). State of IoT 2024: Number of Connected IoT Devices Growing 13% to 18.8 Billion Globally. Available online: https:\/\/iot-analytics.com\/number-connected-iot-devices\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/COMST.2023.3244674","article-title":"Security and Privacy on 6G Network Edge: A Survey","volume":"25","author":"Mao","year":"2023","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MCOMSTD.001.1800036","article-title":"5G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technology","volume":"3","author":"Lin","year":"2019","journal-title":"IEEE Commun. Stand. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MITP.2017.9","article-title":"Technologies for 5G Networks: Challenges and Opportunities","volume":"19","author":"Alani","year":"2017","journal-title":"IT Prof."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MNET.2018.1700202","article-title":"Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing","volume":"32","author":"Li","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xue, H., Huang, B., Qin, M., Zhou, H., and Yang, H. (2020, January 2\u20136). Edge Computing for Internet of Things: A Survey. Proceedings of the 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), Rhodes, Greece.","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00130"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100106","DOI":"10.1016\/j.prime.2023.100106","article-title":"Path loss models for outdoor environment\u2014With a focus on rain attenuation impact on short-range millimeter-wave links","volume":"3","author":"Budalal","year":"2023","journal-title":"E-Prime Adv. Electr. Eng. Electron. Energy"},{"key":"ref_9","unstructured":"Yoon, J., Jordon, J., and Schaar, M. (2018, January 10\u201315). Gain: Missing data imputation using generative adversarial nets. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_10","unstructured":"Muzellec, B., Josse, J., Boyer, C., and Cuturi, M. (2020, January 13\u201318). Missing data imputation using optimal transport. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Luo, Y. (2022). Evaluating the state of the art in missing data imputation for clinical data. Brief. Bioinform., 23.","DOI":"10.1093\/bib\/bbab489"},{"key":"ref_12","first-page":"23806","article-title":"MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms","volume":"34","author":"Kyono","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102675","DOI":"10.1016\/j.cose.2022.102675","article-title":"A systematic literature review of methods and datasets for anomaly-based network intrusion detection","volume":"116","author":"Yang","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"107840","DOI":"10.1016\/j.comnet.2021.107840","article-title":"Machine learning methods for cyber security intrusion detection: Datasets and comparative study","volume":"188","author":"Kilincer","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.ins.2021.05.016","article-title":"Autoencoder-based deep metric learning for network intrusion detection","volume":"569","author":"Andresini","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/TNSM.2020.2966951","article-title":"IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge","volume":"17","author":"Hafeez","year":"2020","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Portela, A.L., Menezes, R.A., Costa, W.L., Silveira, M.M., Bittecnourt, L.F., and Gomes, R.L. (2023, January 8\u201312). Detection of IoT Devices and Network Anomalies based on Anonymized Network Traffic. Proceedings of the NOMS 2023\u20142023 IEEE\/IFIP Network Operations and Management Symposium, Miami, FL, USA.","DOI":"10.1109\/NOMS56928.2023.10154276"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9244","DOI":"10.1109\/JIOT.2023.3323771","article-title":"Toward Continuous Threat Defense: In-Network Traffic Analysis for IoT Gateways","volume":"11","author":"Zang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_20","unstructured":"Kaspersky (2025, March 22). Kaspersky: Attacks on IoT Devices Double in a Year. Available online: https:\/\/iottechnews.com\/news\/2021\/sep\/07\/kaspersky-attacks-on-iot-devices%-double-in-a-year\/."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jain, L.C., Tsihrintzis, G.A., Balas, V.E., and Sharma, D.K. (2020). IoT Botnet: The Largest Threat to the IoT Network. Data Communication and Networks, Springer.","DOI":"10.1007\/978-981-15-0132-6"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Konopa, M., Fesl, J., and Jane\u010dek, J. (2020, January 16\u201318). Promising new Techniques for Computer Network Traffic Classification: A Survey. Proceedings of the 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany.","DOI":"10.1109\/ACIT49673.2020.9208995"},{"key":"ref_23","unstructured":"Kalwar, J.H., and Bhatti, S. (2024). Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.future.2023.09.033","article-title":"Dimensionality reduction for detection of anomalies in the IoT traffic data","volume":"151","author":"Olszewski","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Aversano, L., Bernardi, M.L., Cimitile, M., and Pecori, R. (2021, January 13\u201316). Anomaly Detection of actual IoT traffic flows through Deep Learning. Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Pasadena, CA, USA.","DOI":"10.1109\/ICMLA52953.2021.00275"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.comcom.2021.01.021","article-title":"Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey","volume":"170","author":"Abbasi","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Draper-Gil, G., Lashkari, A.H., Mamun, M.S.I., and Ghorbani, A.A. (2016, January 19\u201321). Characterization of encrypted and vpn traffic using time-related. Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP), Rome, Italy.","DOI":"10.5220\/0005740704070414"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TITS.2019.2910295","article-title":"Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks","volume":"21","author":"Chen","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104740","DOI":"10.1109\/ACCESS.2020.2999662","article-title":"Traffic Data Imputation Using Deep Convolutional Neural Networks","volume":"8","author":"Benkraouda","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shen, Y., Zhang, C., Zhang, S., Yan, J., and Bu, F. (2022, January 25\u201327). LFM-D2GAIN: An Improved Missing Data Imputation Method Based on Generative Adversarial Imputation Nets. Proceedings of the 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Changchun, China.","DOI":"10.1109\/EEBDA53927.2022.9744734"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yu, J., He, Y., and Huang, J.Z. (2021, January 15\u201318). A Two-Stage Missing Value Imputation Method Based on Autoencoder Neural Network. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA.","DOI":"10.1109\/BigData52589.2021.9671338"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107734","DOI":"10.1016\/j.knosys.2021.107734","article-title":"EvoImputer: An evolutionary approach for Missing Data Imputation and feature selection in the context of supervised learning","volume":"236","author":"Awawdeh","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111236","DOI":"10.1016\/j.chaos.2021.111236","article-title":"Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation","volume":"151","author":"Alamoodi","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1613\/jair.1.12312","article-title":"Reviewing autoencoders for missing data imputation: Technical trends, applications and outcomes","volume":"69","author":"Pereira","year":"2020","journal-title":"J. Artif. Intell. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, X. (2021, January 11\u201313). An image classification network for network traffic representation learning. Proceedings of the 2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT), Changsha, China.","DOI":"10.1109\/ISCIPT53667.2021.00078"},{"key":"ref_37","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., and Girshick, R. (2021). Masked autoencoders are scalable vision learners. arXiv.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref_39","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119904","DOI":"10.1109\/ACCESS.2019.2933165","article-title":"PCCN: Parallel Cross Convolutional Neural Network for Abnormal Network Traffic Flows Detection in Multi-Class Imbalanced Network Traffic Flows","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","unstructured":"Orhan, A.E., and Pitkow, X. (2017). Skip connections eliminate singularities. arXiv."},{"key":"ref_42","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_44","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."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/5\/208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:27:15Z","timestamp":1760030835000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/5\/208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,5]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["fi17050208"],"URL":"https:\/\/doi.org\/10.3390\/fi17050208","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2025,5,5]]}}}