{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:39:55Z","timestamp":1763343595129,"version":"3.45.0"},"reference-count":33,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.064270","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:01:03Z","timestamp":1747195263000},"page":"1901-1919","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Detection of Abnormal Traffic Based on SCN-BiLSTM"],"prefix":"10.32604","volume":"84","author":[{"given":"Lulu","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xuehui","family":"Du","sequence":"additional","affiliation":[]},{"given":"Wenjuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Xiangyu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Shihao","family":"Wang","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","first-page":"208","article-title":"A review of research on anomaly traffic detection for software-defined networks","volume":"45","author":"Fu","year":"2024","journal-title":"J Communicat"},{"key":"ref2","first-page":"3254","article-title":"Research on intrusion detection by fusing random forest and gradient boosting tree","volume":"32","author":"Zhou","year":"2021","journal-title":"J Softw"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.future.2022.01.026","article-title":"Imbalanced data classification: a KNN and generative adversarial networks-based hybrid approach for intrusion detection","volume":"131","author":"Ding","year":"2022","journal-title":"Future Gener Comput Syst"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"119189","DOI":"10.1016\/j.eswa.2022.119189","article-title":"A novel adaptive optimization framework for SVM hyper-parameters tuning in non-stationary environment: a case study on intrusion detection system","volume":"213","author":"Jyoti","year":"2023","journal-title":"Expert Syst Appl"},{"key":"ref5","first-page":"501","article-title":"Intrusion detection classification algorithm based on fuzzy SVM model","volume":"37","author":"Wang","year":"2020","journal-title":"Applicat Res Comput"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.comcom.2022.03.009","article-title":"Intrusion detection in cyber-physical environment using hybrid Na\u00efve Bayes\u2014Decision table and multi-objective evolutionary feature selection","volume":"188","author":"Panigrahi","year":"2022","journal-title":"Comput Commun"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"20770","DOI":"10.1038\/s41598-022-23765-x","article-title":"A hybrid interpretable deep structure based on adaptive neuro-fuzzy inference system, decision tree, and K-means for intrusion detection","volume":"12","author":"Liu","year":"2022","journal-title":"Sci Rep"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"102748","DOI":"10.1016\/j.cose.2022.102748","article-title":"A new DDoS attacks intrusion detection model based on deep learning for cybersecurity","volume":"118","author":"Akgun","year":"2022","journal-title":"Comput Secur"},{"key":"ref9","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":"ref10","doi-asserted-by":"crossref","first-page":"121751","DOI":"10.1016\/j.eswa.2023.121751","article-title":"Explainable artificial intelligence for intrusion detection in IoT networks: a deep learning based approach","volume":"238","author":"Sharma","year":"2024","journal-title":"Expert Syst Appl"},{"key":"ref11","series-title":"International Conference on Digital Forensics and Cyber Crime 2022","first-page":"355","article-title":"Deep learning based network intrusion detection system for resource-constrained environments","author":"Rizvi","year":"2022 Nov 16\u201318"},{"key":"ref12","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021","first-page":"1685","article-title":"Equalization loss v2: a new gradient balance approach for long-tailed object detection","author":"Tan","year":"2021 Jun 20\u201325"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1007\/s00607-021-01050-5","article-title":"Machine learning approaches to network intrusion detection for contemporary internet traffic","volume":"104","author":"Ilyas","year":"2022","journal-title":"Computing"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"107810","DOI":"10.1016\/j.compeleceng.2022.107810","article-title":"Anomaly-based intrusion detection system for IoT networks through deep learning model","volume":"99","author":"Saba","year":"2022","journal-title":"Comput Electr Eng"},{"key":"ref15","series-title":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","first-page":"43","article-title":"End-to-end encrypted traffic classification with one-dimensional convolution neural networks","author":"Wang","year":"2017 Jul 22\u201324"},{"key":"ref16","first-page":"132","article-title":"Generative adversarial networks: a survey toward private and secure applications","volume":"54","author":"Cai","year":"2021","journal-title":"CM Comput Surv"},{"key":"ref17","first-page":"858","author":"Li","year":"2017","journal-title":"Neural information processing. ICONIP 2017. Lecture notes in computer science"},{"key":"ref18","series-title":"2019 Cybersecurity and Cyberforensics Conference (CCC)","first-page":"74","article-title":"An improved convolutional neural network model for intrusion detection in networks","author":"Khan","year":"2019 May 8\u20139"},{"key":"ref19","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":"ref20","doi-asserted-by":"crossref","first-page":"115524","DOI":"10.1016\/j.eswa.2021.115524","article-title":"A bidirectional LSTM deep learning approach for intrusion detection","volume":"185","author":"Imrana","year":"2021","journal-title":"Expert Syst Appl"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"4170","DOI":"10.3390\/electronics12194170","article-title":"A network intrusion detection model based on BiLSTM with multi-head attention mechanism","volume":"12","author":"Zhang","year":"2023","journal-title":"Electronics"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/MWC.011.2200320","article-title":"Network abnormal traffic detection framework based on deep reinforcement learning","volume":"31","author":"Dong","year":"2024","journal-title":"IEEE Wirel Commun"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"110216","DOI":"10.1016\/j.asoc.2023.110216","article-title":"Multi-objective evolving long-short term memory networks with attention for network intrusion detection","volume":"139","author":"Wei","year":"2023","journal-title":"Appl Soft Comput"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"100699","DOI":"10.1016\/j.iot.2023.100699","article-title":"A deep learning approach for intrusion detection in Internet of Things using focal loss function","volume":"22","author":"Dina","year":"2023","journal-title":"Intern Things"},{"key":"ref25","series-title":"Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11662","article-title":"Equalization loss for long-tailed object recognition","author":"Tan","year":"2020 Jun 13\u201319"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"103160","DOI":"10.1016\/j.jnca.2021.103160","article-title":"A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique","volume":"191","author":"ElSayed","year":"2021","journal-title":"J Netw Comput Appl"},{"key":"ref27","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"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"64","DOI":"10.26483\/ijarcs.v10i2.6395","article-title":"A review on kdd cup99 and nsl-kdd dataset","volume":"10","author":"Bala","year":"2019","journal-title":"Int J Adv Comput Res"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"2528","DOI":"10.3390\/s19112528","article-title":"Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network","volume":"19","author":"Yang","year":"2019","journal-title":"Sensors"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"142348","DOI":"10.1109\/ACCESS.2021.3120839","article-title":"Study on network intrusion detection method using discrete pre-processing method and convolution neural network","volume":"9","author":"Yoo","year":"2021","journal-title":"IEEE Access"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1007\/s10489-020-01886-y","article-title":"I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems","volume":"51","author":"Bedi","year":"2021","journal-title":"Appl Intell"},{"key":"ref32","first-page":"3719","article-title":"Traffic anomaly detection method based on joint attention mechanism and one-dimensional convolutional neural network-bidirectional long and short-term memory network model","volume":"45","author":"Yin","year":"2023","journal-title":"J Electr Inform Technol"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.comcom.2023.04.018","article-title":"CANET: a hierarchical CNN-attention model for network intrusion detection","volume":"205","author":"Ren","year":"2023","journal-title":"Comput Commun"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-1\/TSP_CMC_64270\/TSP_CMC_64270.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:35:07Z","timestamp":1763343307000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n1\/61757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.064270","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}