{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:26:54Z","timestamp":1759796814884,"version":"build-2065373602"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2022R1F1A1065171","NRF-2021R1G1A1006326","NRF-2022R1G1A1006174"],"award-info":[{"award-number":["NRF-2022R1F1A1065171","NRF-2021R1G1A1006326","NRF-2022R1G1A1006174"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002613","name":"Ulsan National Institute of Science and Technology","doi-asserted-by":"publisher","award":["1.230019"],"award-info":[{"award-number":["1.230019"]}],"id":[{"id":"10.13039\/501100002613","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["2020-0-01336"],"award-info":[{"award-number":["2020-0-01336"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010800","name":"Sungshin Women\u2019s University","doi-asserted-by":"publisher","award":["H20230040"],"award-info":[{"award-number":["H20230040"]}],"id":[{"id":"10.13039\/501100010800","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s10586-025-05396-9","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T14:27:33Z","timestamp":1756909653000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HACOE: hierarchical attack classification with outlier exposure"],"prefix":"10.1007","volume":"28","author":[{"given":"Seongmin","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saerom","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeon-sup","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"5396_CR1","unstructured":"Paxson, V.: Bro: A system for detecting network intruders in Real-Time. In: 7th USENIX Security Symposium (USENIX Security 98). USENIX Association, San Antonio, TX (1998). https:\/\/www.usenix.org\/conference\/7th-usenix-security-symposium\/bro-system-detecting-network-intruders-real-time"},{"key":"5396_CR2","unstructured":"Roesch, M.: Snort - lightweight intrusion detection for networks. In: Proceedings of the 13th USENIX Conference on System Administration. LISA \u201999, pp. 229\u2013238. USENIX Association, USA (1999)"},{"issue":"4","key":"5396_CR3","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/SURV.2008.080406","volume":"10","author":"TTT Nguyen","year":"2008","unstructured":"Nguyen, T.T.T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. EEE Commun. Surv. Tutor. 10(4), 56\u201376 (2008). https:\/\/doi.org\/10.1109\/SURV.2008.080406","journal-title":"EEE Commun. Surv. Tutor."},{"issue":"5","key":"5396_CR4","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MCOM.2019.1800819","volume":"57","author":"S Rezaei","year":"2019","unstructured":"Rezaei, S., Liu, X.: Deep learning for encrypted traffic classification: An overview. IEEE Commun. Mag. 57(5), 76\u201381 (2019)","journal-title":"IEEE Commun. Mag."},{"key":"5396_CR5","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhu, M., Zeng, X., Ye, X., Sheng, Y.: Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking (ICOIN), pp. 712\u2013717 (2017). IEEE","DOI":"10.1109\/ICOIN.2017.7899588"},{"key":"5396_CR6","doi-asserted-by":"publisher","first-page":"45182","DOI":"10.1109\/ACCESS.2019.2908225","volume":"7","author":"Y Zeng","year":"2019","unstructured":"Zeng, Y., Gu, H., Wei, W., Guo, Y.: $$deep-full-range$$: a deep learning based network encrypted traffic classification and intrusion detection framework. IEEE Access 7, 45182\u201345190 (2019)","journal-title":"IEEE Access"},{"key":"5396_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114885","volume":"176","author":"S Dong","year":"2021","unstructured":"Dong, S.: Multi class svm algorithm with active learning for network traffic classification. Expert Syst. Appl. 176, 114885 (2021)","journal-title":"Expert Syst. Appl."},{"key":"5396_CR8","doi-asserted-by":"publisher","unstructured":"Hindy, H., Atkinson, R., Tachtatzis, C., Colin, J.-N., Bayne, E., Bellekens, X.: Utilising deep learning techniques for effective zero-day attack detection. Electronics 9(10) (2020) https:\/\/doi.org\/10.3390\/electronics9101684","DOI":"10.3390\/electronics9101684"},{"issue":"2","key":"5396_CR9","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s10844-022-00747-z","volume":"60","author":"H Hindy","year":"2022","unstructured":"Hindy, H., Tachtatzis, C., Atkinson, R., Brosset, D., Bures, M., Andonovic, I., Michie, C., Bellekens, X.: Leveraging siamese networks for one-shot intrusion detection model. J. Intell. Inf. Syst. 60(2), 407\u2013436 (2022). https:\/\/doi.org\/10.1007\/s10844-022-00747-z. (Special Issue: AI meets Cybersecurity)","journal-title":"J. Intell. Inf. Syst."},{"key":"5396_CR10","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. arXiv preprint arXiv:1812.04606 (2018)"},{"key":"5396_CR11","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., Dai, M.: Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput. netw. 174, 107247 (2020)","journal-title":"Comput. netw."},{"key":"5396_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109542","volume":"253","author":"E Mahdavi","year":"2022","unstructured":"Mahdavi, E., Fanian, A., Mirzaei, A., Taghiyarrenani, Z.: Itl-ids: Incremental transfer learning for intrusion detection systems. Knowl.-Based Syst. 253, 109542 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"5396_CR13","doi-asserted-by":"publisher","first-page":"41525","DOI":"10.1109\/ACCESS.2019.2895334","volume":"7","author":"R Vinayakumar","year":"2019","unstructured":"Vinayakumar, R., Alazab, M., Soman, K., Poornachandran, P., Al-Nemrat, A., Venkatraman, S.: Deep learning approach for intelligent intrusion detection system. Ieee Access 7, 41525\u201341550 (2019)","journal-title":"Ieee Access"},{"key":"5396_CR14","doi-asserted-by":"publisher","unstructured":"Guo, Y.: A review of machine learning-based zero-day attack detection: Challenges and future directions (198) (2023) https:\/\/doi.org\/10.1016\/j.comcom.2022.11.001","DOI":"10.1016\/j.comcom.2022.11.001"},{"key":"5396_CR15","doi-asserted-by":"crossref","unstructured":"Verkerken, M., D\u2019hooge, L., Sudyana, D., Lin, Y.-D., Wauters, T., Volckaert, B., De\u00a0Turck, F.: A novel multi-stage approach for hierarchical intrusion detection. IEEE Transactions on Network and Service Management 20(3), 3915\u20133929 (2023) 10.1109\/TNSM.2023.3259474","DOI":"10.1109\/TNSM.2023.3259474"},{"key":"5396_CR16","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427\u2013436 (2015)","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"5396_CR17","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)"},{"key":"5396_CR18","unstructured":"Ren, J., Fort, S., Liu, J., Roy, A.G., Padhy, S., Lakshminarayanan, B.: A simple fix to mahalanobis distance for improving near-ood detection. arXiv preprint arXiv:2106.09022 (2021)"},{"key":"5396_CR19","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems 31 (2018)"},{"key":"5396_CR20","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1563\u20131572 (2016)","DOI":"10.1109\/CVPR.2016.173"},{"key":"5396_CR21","first-page":"21464","volume":"33","author":"W Liu","year":"2020","unstructured":"Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. Neural Inf. Process. Syst. 33, 21464\u201321475 (2020)","journal-title":"Neural Inf. Process. Syst."},{"key":"5396_CR22","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of International Conference on Information Systems Security and Privacy (2018)","DOI":"10.5220\/0006639801080116"},{"key":"5396_CR23","doi-asserted-by":"publisher","first-page":"132911","DOI":"10.1109\/ACCESS.2020.3009843","volume":"8","author":"D Stiawan","year":"2020","unstructured":"Stiawan, D., Idris, M.Y.B., Bamhdi, A.M., Budiarto, R., et al.: Cicids-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access 8, 132911\u2013132921 (2020)","journal-title":"IEEE Access"},{"key":"5396_CR24","volume":"50","author":"MA Ferrag","year":"2020","unstructured":"Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. J. Inf. Secur Appl. 50, 102419 (2020)","journal-title":"J. Inf. Secur Appl."},{"key":"5396_CR25","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.cose.2019.06.005","volume":"86","author":"M Ring","year":"2019","unstructured":"Ring, M., Wunderlich, S., Scheuring, D., Landes, D., Hotho, A.: A survey of network-based intrusion detection data sets. Comput. Secur. 86, 147\u2013167 (2019)","journal-title":"Comput. Secur."},{"key":"5396_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102767","volume":"169","author":"S Gamage","year":"2020","unstructured":"Gamage, S., Samarabandu, J.: Deep learning methods in network intrusion detection: A survey and an objective comparison. J. Netw. Comput. Appl. 169, 102767 (2020)","journal-title":"J. Netw. Comput. Appl."},{"key":"5396_CR27","unstructured":"Lashkari, A.H., Zang, Y., Owhuo, G., Mamun, M., Gil, G.: Cicflowmeter. GitHub.[vid. 2021-08-10]. Dostupn\u00e9 z: https:\/\/github. com\/ahlashkari\/CICFlowMeter\/blob\/master\/ReadMe. txt (2017)"},{"key":"5396_CR28","doi-asserted-by":"publisher","unstructured":"Kang, H., Ahn, D.H., Lee, G.M., Yoo, J.D., Park, K.H., Kim, H.K.: IoT Network Intrusion Dataset. https:\/\/doi.org\/10.21227\/q70p-q449","DOI":"10.21227\/q70p-q449"},{"key":"5396_CR29","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the kdd cup 99 data set. In: Proceedings of the Second IEEE International Conference on Computational Intelligence for Security and Defense Applications. CISDA\u201909, pp. 53\u201358. IEEE Press, ??? (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"5396_CR30","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch. In: NIPS 2017 Workshop on Autodiff (2017). https:\/\/openreview.net\/forum?id=BJJsrmfCZ"},{"key":"5396_CR31","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.E.: When does label smoothing help? Advances in neural information processing systems 32 (2019)"},{"key":"5396_CR32","doi-asserted-by":"publisher","unstructured":"de Souza, C.A., Westphall, C.B., Valencio, J.D.G., Machado, R.B., R.\u00a0Bezerra, W.: Hierarchical multistep approach for intrusion detection and identification in iot and fog computing-based environments. Ad Hoc Networks 161, 103541 (2024) https:\/\/doi.org\/10.1016\/j.adhoc.2024.103541","DOI":"10.1016\/j.adhoc.2024.103541"},{"key":"5396_CR33","doi-asserted-by":"crossref","unstructured":"Han, J., Kim, S., Ha, J., Han, D.: Sgx-box: Enabling visibility on encrypted traffic using a secure middlebox module. In: Proceedings of the First Asia-Pacific Workshop on Networking, pp. 99\u2013105 (2017)","DOI":"10.1145\/3106989.3106994"},{"key":"5396_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102538","volume":"154","author":"H Tahaei","year":"2020","unstructured":"Tahaei, H., Afifi, F., Asemi, A., Zaki, F., Anuar, N.B.: The rise of traffic classification in iot networks: A survey. J. Netw. Comput. Appl. 154, 102538 (2020)","journal-title":"J. Netw. Comput. Appl."},{"key":"5396_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102662","volume":"163","author":"GDLT Parra","year":"2020","unstructured":"Parra, G.D.L.T., Rad, P., Choo, K.-K.R., Beebe, N.: Detecting internet of things attacks using distributed deep learning. J. Netw. Comput. Appl. 163, 102662 (2020)","journal-title":"J. Netw. Comput. Appl."},{"key":"5396_CR36","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.future.2021.03.024","volume":"122","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Li, J.-L., Liu, X.-M., Dong, C.: Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection. Future Gener. Comput. Syst. 122, 130\u2013143 (2021)","journal-title":"Future Gener. Comput. Syst."},{"issue":"8","key":"5396_CR37","doi-asserted-by":"publisher","first-page":"5810","DOI":"10.1109\/TII.2020.3038761","volume":"17","author":"C Luo","year":"2020","unstructured":"Luo, C., Tan, Z., Min, G., Gan, J., Shi, W., Tian, Z.: A novel web attack detection system for internet of things via ensemble classification. EEE Trans. Industr. Inform. 17(8), 5810\u20135818 (2020)","journal-title":"EEE Trans. Industr. Inform."},{"key":"5396_CR38","doi-asserted-by":"crossref","unstructured":"Vyas, A., Jammalamadaka, N., Zhu, X., Das, D., Kaul, B., Willke, T.L.: Out-of-distribution detection using an ensemble of self supervised leave-out classifiers. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 550\u2013564 (2018)","DOI":"10.1007\/978-3-030-01237-3_34"},{"key":"5396_CR39","unstructured":"D\u2019Angelo, F., Henning, C.: Uncertainty-based out-of-distribution detection requires suitable function space priors (2021)"},{"key":"5396_CR40","doi-asserted-by":"crossref","unstructured":"Soltani, M., Ousat, B., Jafari Siavoshani, M., Jahangir, A.H.: An adaptable deep learning-based intrusion detection system to zero-day attacks. Journal of Information Security and Applications 76, 103516 (2023) 10.1016\/j.jisa.2023.103516","DOI":"10.1016\/j.jisa.2023.103516"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05396-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05396-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05396-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T09:36:37Z","timestamp":1759743397000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05396-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"references-count":40,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["5396"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05396-9","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,9,3]]},"assertion":[{"value":"28 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"674"}}