{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:23:41Z","timestamp":1773156221995,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T00:00:00Z","timestamp":1749945600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T00:00:00Z","timestamp":1749945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cybersecurity"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Network Intrusion Detection Systems (NIDS) are essential for safeguarding networks against malicious activities. However, existing machine learning-based NIDS often require complex feature engineering, which demands significant domain expertise and experimentation, leading to suboptimal model performance in complex network environments. In contrast, deep learning approaches, while powerful, struggle with imbalanced data, resulting in a bias towards normal traffic and reduced effectiveness in detecting rare attacks. To address these issues, we propose a method that combines contrastive learning and Bayesian Gaussian Mixture Model (BGMM). Specifically, we propose a novel contrastive learning loss that enables the model to automatically learn the similarity within normal traffic and the distinction between normal and malicious traffic, thereby generating robust and distinguishable feature representations. This approach not only eliminates the need for manual feature engineering but also helps alleviate the issue of weak feature representations for rare attacks. BGMM further enhances detection performance by adapting to both normal and malicious patterns through the use of multiple components. The effectiveness of the proposed method is validated through extensive experiments on two widely used modern network intrusion datasets. On the UNSW-NB15 dataset, the proposed method achieves 91.27% accuracy and 92.30% F1-score, which is 1.85% and 2.35% better than the state-of-the-art (SOTA) method. On the Distrinet-CIC-IDS2017 dataset, the proposed method achieves 99.66% accuracy and 99.12% F1-score, which is 0.05% and 0.12% better than the SOTA method.<\/jats:p>","DOI":"10.1186\/s42400-025-00364-7","type":"journal-article","created":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T01:01:39Z","timestamp":1749949299000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A network intrusion detection method based on contrastive learning and Bayesian Gaussian Mixture Model"],"prefix":"10.1186","volume":"8","author":[{"given":"Liyou","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9332-5258","authenticated-orcid":false,"given":"Ming","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,15]]},"reference":[{"issue":"10","key":"364_CR1","doi-asserted-by":"publisher","first-page":"10611","DOI":"10.1007\/s11227-023-05073-x","volume":"79","author":"A Abdelkhalek","year":"2023","unstructured":"Abdelkhalek A, Mashaly M (2023) Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning. J Supercomput 79(10):10611\u201310644. https:\/\/doi.org\/10.1007\/s11227-023-05073-x","journal-title":"J Supercomput"},{"issue":"1","key":"364_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05073-x","volume":"32","author":"Z Ahmad","year":"2021","unstructured":"Ahmad Z, Shahid Khan A, Wai Shiang C et al (2021) Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol 32(1):e4150. https:\/\/doi.org\/10.1007\/s11227-023-05073-x","journal-title":"Trans Emerg Telecommun Technol"},{"issue":"10","key":"364_CR3","doi-asserted-by":"publisher","first-page":"5015","DOI":"10.3390\/app12105015","volume":"12","author":"K Albulayhi","year":"2022","unstructured":"Albulayhi K, Abu Al-Haija Q, Alsuhibany SA et al (2022) Iot intrusion detection using machine learning with a novel high performing feature selection method. Appl Sci 12(10):5015. https:\/\/doi.org\/10.3390\/app12105015","journal-title":"Appl Sci"},{"key":"364_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jestch.2022.101322","volume":"38","author":"HC Altunay","year":"2023","unstructured":"Altunay HC, Albayrak Z (2023) A hybrid cnn+ lstm-based intrusion detection system for industrial iot networks. Eng Sci Technol Int J 38:101322. https:\/\/doi.org\/10.1016\/j.jestch.2022.101322","journal-title":"Eng Sci Technol Int J"},{"key":"364_CR5","unstructured":"An C, Feng J, Lv K et al (2022) Cont: contrastive neural text generation. Adv Neural Inform Process Syst 35:2197\u20132210 https:\/\/doi.org\/10.48550\/arXiv.2205.14690"},{"key":"364_CR6","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.future.2021.04.017","volume":"123","author":"G Andresini","year":"2021","unstructured":"Andresini G, Appice A, De Rose L et al (2021) Gan augmentation to deal with imbalance in imaging-based intrusion detection. Futur Gener Comput Syst 123:108\u2013127. https:\/\/doi.org\/10.1016\/j.future.2021.04.017","journal-title":"Futur Gener Comput Syst"},{"issue":"16","key":"364_CR7","doi-asserted-by":"publisher","first-page":"5986","DOI":"10.3390\/s22165986","volume":"22","author":"AK Balyan","year":"2022","unstructured":"Balyan AK, Ahuja S, Lilhore UK et al (2022) A hybrid intrusion detection model using ega-pso and improved random forest method. Sensors 22(16):5986. https:\/\/doi.org\/10.3390\/s22165986","journal-title":"Sensors"},{"key":"364_CR8","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning. Springer, Cham"},{"issue":"518","key":"364_CR9","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","volume":"112","author":"DM Blei","year":"2017","unstructured":"Blei DM, Kucukelbir A, McAuliffe JD (2017) Variational inference: a review for statisticians. J Am Stat Assoc 112(518):859\u2013877. https:\/\/doi.org\/10.1080\/01621459.2017.1285773","journal-title":"J Am Stat Assoc"},{"key":"364_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3384528","author":"W Dai","year":"2024","unstructured":"Dai W, Li X, Ji W et al (2024) Network intrusion detection method based on cnn, bilstm, and attention mechanism. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3384528","journal-title":"IEEE Access"},{"issue":"1","key":"364_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J Roy Stat Soc: Ser B (Methodol) 39(1):1\u201322. https:\/\/doi.org\/10.1111\/j.2517-6161.1977.tb01600.x","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"1","key":"364_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42400-021-00103-8","volume":"5","author":"RA Disha","year":"2022","unstructured":"Disha RA, Waheed S (2022) Performance analysis of machine learning models for intrusion detection system using gini impurity-based weighted random forest (giwrf) feature selection technique. Cybersecurity 5(1):1. https:\/\/doi.org\/10.1186\/s42400-021-00103-8","journal-title":"Cybersecurity"},{"issue":"6","key":"364_CR13","doi-asserted-by":"publisher","first-page":"898","DOI":"10.3390\/electronics11060898","volume":"11","author":"Y Fu","year":"2022","unstructured":"Fu Y, Du Y, Cao Z et al (2022) A deep learning model for network intrusion detection with imbalanced data. Electronics 11(6):898. https:\/\/doi.org\/10.3390\/electronics11060898","journal-title":"Electronics"},{"key":"364_CR14","doi-asserted-by":"publisher","unstructured":"Gao T, Yao X, Chen D (2021) Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.552","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"364_CR15","doi-asserted-by":"publisher","first-page":"1230593","DOI":"10.1155\/2021\/1230593","volume":"1","author":"A Guezzaz","year":"2021","unstructured":"Guezzaz A, Benkirane S, Azrour M et al (2021) A reliable network intrusion detection approach using decision tree with enhanced data quality. Security Commun Netw 1:1230593. https:\/\/doi.org\/10.1155\/2021\/1230593","journal-title":"Security Commun Netw"},{"key":"364_CR16","doi-asserted-by":"publisher","first-page":"99837","DOI":"10.1109\/ACCESS.2022.3206425","volume":"10","author":"A Halbouni","year":"2022","unstructured":"Halbouni A, Gunawan TS, Habaebi MH et al (2022) Cnn-lstm: hybrid deep neural network for network intrusion detection system. IEEE Access 10:99837\u201399849. https:\/\/doi.org\/10.1109\/ACCESS.2022.3206425","journal-title":"IEEE Access"},{"key":"364_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102448","volume":"110","author":"Z Halim","year":"2021","unstructured":"Halim Z, Yousaf MN, Waqas M et al (2021) An effective genetic algorithm-based feature selection method for intrusion detection systems. Comput Security 110:102448. https:\/\/doi.org\/10.1016\/j.cose.2021.102448","journal-title":"Comput Security"},{"key":"364_CR18","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2023.01.072","volume":"530","author":"J He","year":"2023","unstructured":"He J, Wang X, Song Y et al (2023) A multiscale intrusion detection system based on pyramid depthwise separable convolution neural network. Neurocomputing 530:48\u201359. https:\/\/doi.org\/10.1016\/j.neucom.2023.01.072","journal-title":"Neurocomputing"},{"issue":"4","key":"364_CR19","first-page":"569","volume":"23","author":"J Hu","year":"2021","unstructured":"Hu J, Liu C, Cui Y (2021) An improved cnn approach for network intrusion detection system. Int J Netw Security 23(4):569\u2013575","journal-title":"Int J Netw Security"},{"key":"364_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2020.102177","volume":"105","author":"S Huang","year":"2020","unstructured":"Huang S, Lei K (2020) Igan-ids: an imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks. Ad Hoc Netw 105:102177. https:\/\/doi.org\/10.1016\/j.adhoc.2020.102177","journal-title":"Ad Hoc Netw"},{"issue":"1","key":"364_CR21","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/technologies9010002","volume":"9","author":"A Jaiswal","year":"2020","unstructured":"Jaiswal A, Babu AR, Zadeh MZ et al (2020) A survey on contrastive self-supervised learning. Technologies 9(1):2. https:\/\/doi.org\/10.3390\/technologies9010002","journal-title":"Technologies"},{"key":"364_CR22","doi-asserted-by":"publisher","first-page":"58392","DOI":"10.1109\/ACCESS.2020.2982418","volume":"8","author":"H Jiang","year":"2020","unstructured":"Jiang H, He Z, Ye G et al (2020) Network intrusion detection based on pso-xgboost model. IEEE Access 8:58392\u201358401. https:\/\/doi.org\/10.1109\/ACCESS.2020.2982418","journal-title":"IEEE Access"},{"key":"364_CR23","doi-asserted-by":"publisher","unstructured":"Jin S, Chung JG, Xu Y (2021) Signature-based intrusion detection system (ids) for in-vehicle can bus network. In: 2021 IEEE international symposium on circuits and systems (ISCAS), IEEE, pp 1\u20135, https:\/\/doi.org\/10.1109\/ISCAS51556.2021.9401087","DOI":"10.1109\/ISCAS51556.2021.9401087"},{"issue":"1","key":"364_CR24","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/s40537-020-00379-6","volume":"7","author":"SM Kasongo","year":"2020","unstructured":"Kasongo SM, Sun Y (2020) Performance analysis of intrusion detection systems using a feature selection method on the unsw-nb15 dataset. J Big Data 7(1):105. https:\/\/doi.org\/10.1186\/s40537-020-00379-6","journal-title":"J Big Data"},{"issue":"4","key":"364_CR25","doi-asserted-by":"publisher","first-page":"571","DOI":"10.3390\/math12040571","volume":"12","author":"D Kilichev","year":"2024","unstructured":"Kilichev D, Turimov D, Kim W (2024) Next-generation intrusion detection for iot evcs: integrating cnn, lstm, and gru models. Mathematics 12(4):571. https:\/\/doi.org\/10.3390\/math12040571","journal-title":"Mathematics"},{"issue":"1","key":"364_CR26","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s00779-019-01332-y","volume":"25","author":"J Lee","year":"2021","unstructured":"Lee J, Park K (2021) Gan-based imbalanced data intrusion detection system. Pers Ubiquit Comput 25(1):121\u2013128. https:\/\/doi.org\/10.1007\/s00779-019-01332-y","journal-title":"Pers Ubiquit Comput"},{"issue":"1","key":"364_CR27","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s40537-024-00892-y","volume":"11","author":"J Li","year":"2024","unstructured":"Li J, Othman MS, Chen H et al (2024) Optimizing iot intrusion detection system: feature selection versus feature extraction in machine learning. J Big Data 11(1):36","journal-title":"J Big Data"},{"issue":"7","key":"364_CR28","doi-asserted-by":"publisher","first-page":"2122","DOI":"10.3390\/s24072122","volume":"24","author":"L Li","year":"2024","unstructured":"Li L, Lu Y, Yang G et al (2024) End-to-end network intrusion detection based on contrastive learning. Sensors 24(7):2122. https:\/\/doi.org\/10.3390\/s24072122","journal-title":"Sensors"},{"key":"364_CR29","doi-asserted-by":"publisher","unstructured":"Li S, Xia X, Ge S, et\u00a0al (2022) Selective-supervised contrastive learning with noisy labels. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 316\u2013325, https:\/\/doi.org\/10.48550\/arXiv.2203.04181","DOI":"10.48550\/arXiv.2203.04181"},{"issue":"2","key":"364_CR30","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1109\/TR.2022.3164877","volume":"71","author":"C Liu","year":"2022","unstructured":"Liu C, Antypenko R, Sushko I et al (2022) Intrusion detection system after data augmentation schemes based on the vae and cvae. IEEE Trans Reliab 71(2):1000\u20131010. https:\/\/doi.org\/10.1109\/TR.2022.3164877","journal-title":"IEEE Trans Reliab"},{"key":"364_CR31","doi-asserted-by":"publisher","unstructured":"Liu C, Wen J, Luo X, et\u00a0al (2023) Dicnet: Deep instance-level contrastive network for double incomplete multi-view multi-label classification. In: Proceedings of the AAAI conference on artificial intelligence, pp 8807\u20138815, https:\/\/doi.org\/10.1609\/aaai.v37i7.26059","DOI":"10.1609\/aaai.v37i7.26059"},{"key":"364_CR32","doi-asserted-by":"publisher","unstructured":"Liu L, Engelen G, Lynar T, et\u00a0al (2022b) Error prevalence in nids datasets: A case study on cic-ids-2017 and cse-cic-ids-2018. In: 2022 IEEE Conference on Communications and Network Security (CNS), IEEE, pp 254\u2013262, https:\/\/doi.org\/10.1109\/CNS56114.2022.9947235","DOI":"10.1109\/CNS56114.2022.9947235"},{"key":"364_CR33","doi-asserted-by":"publisher","first-page":"6634811","DOI":"10.1155\/2021\/6634811","volume":"1","author":"B Mahbooba","year":"2021","unstructured":"Mahbooba B, Timilsina M, Sahal R et al (2021) Explainable artificial intelligence (xai) to enhance trust management in intrusion detection systems using decision tree model. Complexity 1:6634811. https:\/\/doi.org\/10.1155\/2021\/6634811","journal-title":"Complexity"},{"key":"364_CR34","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J (2015) Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In: 2015 military communications and information systems conference (MilCIS), IEEE, pp 1\u20136, https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"364_CR35","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.comcom.2023.04.018","volume":"205","author":"K Ren","year":"2023","unstructured":"Ren K, Yuan S, Zhang C et al (2023) Canet: a hierarchical cnn-attention model for network intrusion detection. Comput Commun 205:170\u2013181. https:\/\/doi.org\/10.1016\/j.comcom.2023.04.018","journal-title":"Comput Commun"},{"key":"364_CR36","first-page":"108","volume":"1","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin I, Lashkari AH, Ghorbani AA et al (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1:108\u2013116","journal-title":"ICISSp"},{"issue":"6","key":"364_CR37","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1080\/19393555.2021.1975853","volume":"31","author":"T Sommestad","year":"2022","unstructured":"Sommestad T, Holm H, Steinvall D (2022) Variables influencing the effectiveness of signature-based network intrusion detection systems. Inform Security J Global Perspective 31(6):711\u2013728. https:\/\/doi.org\/10.1080\/19393555.2021.1975853","journal-title":"Inform Security J Global Perspective"},{"key":"364_CR38","doi-asserted-by":"publisher","first-page":"29575","DOI":"10.1109\/ACCESS.2020.2972627","volume":"8","author":"T Su","year":"2020","unstructured":"Su T, Sun H, Zhu J et al (2020) Bat: deep learning methods on network intrusion detection using nsl-kdd dataset. IEEE Access 8:29575\u201329585. https:\/\/doi.org\/10.1109\/ACCESS.2020.2972627","journal-title":"IEEE Access"},{"issue":"3","key":"364_CR39","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.3390\/s23031141","volume":"23","author":"B Tang","year":"2023","unstructured":"Tang B, Lu Y, Li Q et al (2023) A diffusion model based on network intrusion detection method for industrial cyber-physical systems. Sensors 23(3):1141. https:\/\/doi.org\/10.3390\/s23031141","journal-title":"Sensors"},{"key":"364_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103587","volume":"137","author":"AV Turukmane","year":"2024","unstructured":"Turukmane AV, Devendiran R (2024) M-multisvm: an efficient feature selection assisted network intrusion detection system using machine learning. Comput Security 137:103587. https:\/\/doi.org\/10.1016\/j.cose.2023.103587","journal-title":"Comput Security"},{"key":"364_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119589","volume":"217","author":"TB Viana","year":"2023","unstructured":"Viana TB, Souza VL, Oliveira AL et al (2023) A multi-task approach for contrastive learning of handwritten signature feature representations. Expert Syst Appl 217:119589. https:\/\/doi.org\/10.1016\/j.eswa.2023.119589","journal-title":"Expert Syst Appl"},{"key":"364_CR42","doi-asserted-by":"publisher","unstructured":"Wang N, Chen Y, Hu Y, et\u00a0al (2022a) Feco: Boosting intrusion detection capability in iot networks via contrastive learning. In: IEEE INFOCOM 2022-IEEE Conference on Computer Communications, IEEE, pp 1409\u20131418, https:\/\/doi.org\/10.1109\/INFOCOM48880.2022.9796926","DOI":"10.1109\/INFOCOM48880.2022.9796926"},{"key":"364_CR43","doi-asserted-by":"publisher","unstructured":"Wang Y, Li J, Wang H, et\u00a0al (2022b) Wav2vec-switch: Contrastive learning from original-noisy speech pairs for robust speech recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 7097\u20137101, https:\/\/doi.org\/10.48550\/arXiv.2110.04934","DOI":"10.48550\/arXiv.2110.04934"},{"issue":"7","key":"364_CR44","doi-asserted-by":"publisher","first-page":"3087","DOI":"10.1002\/int.22397","volume":"36","author":"C Wu","year":"2021","unstructured":"Wu C, Li W (2021) Enhancing intrusion detection with feature selection and neural network. Int J Intell Syst 36(7):3087\u20133105. https:\/\/doi.org\/10.1002\/int.22397","journal-title":"Int J Intell Syst"},{"key":"364_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102675","volume":"116","author":"Z Yang","year":"2022","unstructured":"Yang Z, Liu X, Li T et al (2022) A systematic literature review of methods and datasets for anomaly-based network intrusion detection. Comput Security 116:102675. https:\/\/doi.org\/10.1016\/j.cose.2022.102675","journal-title":"Comput Security"},{"issue":"1","key":"364_CR46","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s40537-023-00694-8","volume":"10","author":"Y Yin","year":"2023","unstructured":"Yin Y, Jang-Jaccard J, Xu W et al (2023) Igrf-rfe: a hybrid feature selection method for mlp-based network intrusion detection on unsw-nb15 dataset. J Big Data 10(1):15. https:\/\/doi.org\/10.1186\/s40537-023-00694-8","journal-title":"J Big Data"},{"key":"364_CR47","doi-asserted-by":"publisher","unstructured":"Yuan L, Sun J, Zhuang S, et\u00a0al (2024) Manticore: An unsupervised intrusion detection system based on contrastive learning in 5g networks. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 4705\u20134709, https:\/\/doi.org\/10.1109\/ICASSP48485.2024.10447814","DOI":"10.1109\/ICASSP48485.2024.10447814"},{"issue":"4","key":"364_CR48","doi-asserted-by":"publisher","first-page":"4232","DOI":"10.1109\/TNSM.2022.3218843","volume":"19","author":"Y Yue","year":"2022","unstructured":"Yue Y, Chen X, Han Z et al (2022) Contrastive learning enhanced intrusion detection. IEEE Trans Netw Serv Manage 19(4):4232\u20134247. https:\/\/doi.org\/10.1109\/TNSM.2022.3218843","journal-title":"IEEE Trans Netw Serv Manage"},{"key":"364_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107315","volume":"177","author":"H Zhang","year":"2020","unstructured":"Zhang H, Huang L, Wu CQ et al (2020) An effective convolutional neural network based on smote and gaussian mixture model for intrusion detection in imbalanced dataset. Comput Netw 177:107315. https:\/\/doi.org\/10.1016\/j.comnet.2020.107315","journal-title":"Comput Netw"},{"key":"364_CR50","doi-asserted-by":"publisher","unstructured":"Zhang X, Zhao R, Jiang Z, et\u00a0al (2024) Aoc-ids: Autonomous online framework with contrastive learning for intrusion detection. arXiv preprint arXiv:2402.01807. https:\/\/doi.org\/10.48550\/arXiv.2402.01807","DOI":"10.48550\/arXiv.2402.01807"},{"key":"364_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107247","volume":"174","author":"Y Zhou","year":"2020","unstructured":"Zhou Y, Cheng G, Jiang S et al (2020) Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput Netw 174:107247. https:\/\/doi.org\/10.1016\/j.comnet.2020.107247","journal-title":"Comput Netw"},{"issue":"5","key":"364_CR52","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11161","volume":"5","author":"A Zimek","year":"2012","unstructured":"Zimek A, Schubert E, Kriegel HP (2012) A survey on unsupervised outlier detection in high-dimensional numerical data. Stat Anal Data Mining ASA Data Sci J 5(5):363\u2013387. https:\/\/doi.org\/10.1002\/sam.11161","journal-title":"Stat Anal Data Mining ASA Data Sci J"}],"container-title":["Cybersecurity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-025-00364-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42400-025-00364-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-025-00364-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T01:01:41Z","timestamp":1749949301000},"score":1,"resource":{"primary":{"URL":"https:\/\/cybersecurity.springeropen.com\/articles\/10.1186\/s42400-025-00364-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,15]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["364"],"URL":"https:\/\/doi.org\/10.1186\/s42400-025-00364-7","relation":{},"ISSN":["2523-3246"],"issn-type":[{"value":"2523-3246","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,15]]},"assertion":[{"value":"11 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"59"}}