{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T07:01:15Z","timestamp":1764831675847,"version":"3.46.0"},"reference-count":25,"publisher":"Tech Science Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.067211","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T07:59:53Z","timestamp":1757318393000},"page":"2893-2912","source":"Crossref","is-referenced-by-count":0,"title":["Adapting Convolutional Autoencoder for DDoS Attack Detection via Joint Reconstruction Learning and Refined Anomaly Scoring"],"prefix":"10.32604","volume":"85","author":[{"given":"Seulki","family":"Han","sequence":"first","affiliation":[]},{"given":"Sangho","family":"Son","sequence":"additional","affiliation":[]},{"given":"Won","family":"Sakong","sequence":"additional","affiliation":[]},{"given":"Haemin","family":"Jung","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","series-title":"2003 Symposium on Security and Privacy; 2003 May 11\u201314","first-page":"93","article-title":"Pi: a path identification mechanism to defend against DDoS attacks","author":"Yaar"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref3","first-page":"3271","article-title":"Enhanced deep autoencoder based feature representation learning for intelligent intrusion detection system","volume":"68","author":"Vaiyapuri","year":"2021","journal-title":"Comput Mater Contin"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.neucom.2020.04.057","article-title":"An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges","volume":"404","author":"Charte","year":"2020","journal-title":"Neurocomputing"},{"key":"ref5","series-title":"Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis. 2014 Dec 2","first-page":"4","article-title":"Anomaly detection using autoencoders with nonlinear dimensionality reduction","author":"Sakurada"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"70884","DOI":"10.1109\/ACCESS.2018.2881003","article-title":"Anomaly detection for HTTP using convolutional autoencoders","volume":"6","author":"Park","year":"2018","journal-title":"IEEE Access"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"7103","DOI":"10.1002\/int.22582","article-title":"Improved autoencoder for unsupervised anomaly detection","volume":"36","author":"Cheng","year":"2021","journal-title":"Int J Intell Syst"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.inffus.2017.12.007","article-title":"A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines","volume":"44","author":"Charte","year":"2018","journal-title":"Inf Fusion"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"53346","DOI":"10.1109\/ACCESS.2020.2980937","article-title":"Multi-channel deep feature learning for intrusion detection","volume":"8","author":"Andresini","year":"2020","journal-title":"IEEE Access"},{"key":"ref10","first-page":"3371","article-title":"Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref11","series-title":"2017 International Joint Conference on Neural Networks (IJCNN); 2017 May 14\u201319","first-page":"911","article-title":"Asymmetric stacked autoencoder","author":"Majumdar"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/TNSM.2022.3229706","article-title":"ARCADE: adversarially regularized convolutional autoencoder for network anomaly detection","volume":"20","author":"Lunardi","year":"2023","journal-title":"IEEE Trans Netw Serv Manage"},{"article-title":"Rapp: novelty detection with reconstruction along projection pathway","series-title":"International Conference on Learning Representations; 2020 Apr 30","author":"Kim","key":"ref13"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"82443","DOI":"10.1109\/ACCESS.2024.3404778","article-title":"Securing the IoT cyber environment: enhancing intrusion anomaly detection with vision transformers","volume":"12","author":"Sana","year":"2024","journal-title":"IEEE Access"},{"journal-title":"Forecasting: principles and practice","year":"2018","author":"Hyndman","key":"ref15"},{"key":"ref16","series-title":"The 4th International Conference on Modelling of Mechanical and Mechatronic Systems; 2011 Sep 20\u201322","first-page":"380","article-title":"The simple exponential smoothing model","author":"Ostertagov\u00e1"},{"key":"ref17","series-title":"2015 IEEE Trustcom\/BigDataSE\/ISPA; 2015 Aug 20\u201322","first-page":"310","article-title":"An entropy-based distributed DDoS detection mechanism in software-defined networking","author":"Wang"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1080\/09540091.2015.1031082","article-title":"Distributed reinforcement learning for adaptive and robust network intrusion response","volume":"27","author":"Malialis","year":"2015","journal-title":"Connect Sci"},{"key":"ref19","series-title":"2018 IEEE International Conference on Communications (ICC); 2018 May 20\u201324","first-page":"1","article-title":"Machine-learning-based online distributed denial-of-service attack detection using spark streaming","author":"Zhou"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.3390\/s24051418","article-title":"Novel machine learning approach for DDoS cloud detection: bayesian-based CNN and data fusion enhancements","volume":"24","author":"AlSaleh","year":"2024","journal-title":"Sensors"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/TNSM.2021.3088326","article-title":"Chronos: DDOS attack detection using time-based autoencoder","volume":"19","author":"Salahuddin","year":"2022","journal-title":"IEEE Trans Netw Serv Manag"},{"key":"ref22","series-title":"ESANN 2015: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; 2015 Apr 22\u201324","first-page":"89","article-title":"Long short term memory networks for anomaly detection in time series","author":"Malhotra"},{"key":"ref23","series-title":"2008 Eighth IEEE International Conference on Data Mining; 2008 Dec 15\u201319","first-page":"413","article-title":"Isolation forest","author":"Liu"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","article-title":"A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder","volume":"3","author":"Park","year":"2018","journal-title":"IEEE Robot Autom Lett"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"126982","DOI":"10.1016\/j.eswa.2025.126982","article-title":"LLM-AE-MP: web attack detection using a large language model with autoencoder and multilayer perceptron","volume":"274","author":"Yang","year":"2025","journal-title":"Expert Syst Appl"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-2\/TSP_CMC_67211\/TSP_CMC_67211.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T05:35:39Z","timestamp":1764826539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n2\/63815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":25,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.067211","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}