{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:38:38Z","timestamp":1775745518951,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T00:00:00Z","timestamp":1726531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean government (MSIT)","award":["RS-2023-00220303"],"award-info":[{"award-number":["RS-2023-00220303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these advanced models is the increased training time as model complexity grows, and the symmetry between performance and training time must be taken into account. To address this issue, this study proposes a fast-persistent-contrastive-divergence-based deep belief network (FPCD-DBN) that offers both high accuracy and rapid training times. This model combines the efficiency of contrastive divergence with the powerful feature extraction capabilities of deep belief networks. While traditional deep belief networks use a contrastive divergence (CD) algorithm, the FPCD algorithm improves the performance of the model by passing the results of each detection layer to the next layer. In addition, the mix of parameter updates using fast weights and continuous chains makes the model fast and accurate. The performance of the proposed FPCD-DBN model was evaluated on several benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS-2017. As a result, the proposed method proved to be a viable solution as the model performed well with an accuracy of 89.4% and an F1 score of 89.7%. By achieving superior performance across multiple datasets, the approach shows great potential for enhancing network security and providing a robust defense against evolving cyber threats.<\/jats:p>","DOI":"10.3390\/sym16091220","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:51:43Z","timestamp":1726620703000},"page":"1220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Study on Network Anomaly Detection Using Fast Persistent Contrastive Divergence"],"prefix":"10.3390","volume":"16","author":[{"given":"Jaeyeong","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"},{"name":"Convergence Major for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2519-0707","authenticated-orcid":false,"given":"Seongmin","family":"Park","sequence":"additional","affiliation":[{"name":"Infrastructure Security Technology Team, Korea Internet & Security Agency, Naju-si 58324, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joonhyung","family":"Lim","sequence":"additional","affiliation":[{"name":"Infrastructure Security Technology Team, Korea Internet & Security Agency, Naju-si 58324, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiwon","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"},{"name":"Cyber Warfare Research Institute, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8621-715X","authenticated-orcid":false,"given":"Dongil","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2665-3339","authenticated-orcid":false,"given":"Dongkyoo","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"},{"name":"Convergence Major for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea"},{"name":"Cyber Warfare Research Institute, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"ref_1","unstructured":"Barbir, A., Murphy, S., and Yang, Y. (2024, September 03). RFC 4593: Generic Threats to Routing Protocols. Available online: http:\/\/www.ietf.org\/rfc\/rfc4593.txt."},{"key":"ref_2","unstructured":"(2024, September 03). Cisco Annual Internet Report (2018\u20132023) White Paper. Available online: https:\/\/www.cisco.com\/c\/en\/us\/solutions\/collateral\/executive-perspectives\/annual-internet-report\/white-paper-c11-741490.html."},{"key":"ref_3","unstructured":"Bace, R.G., and Mell, P. (2024, September 09). Intrusion Detection Systems, Available online: https:\/\/www.nist.gov\/publications\/intrusion-detection-systems."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jnca.2012.09.004","article-title":"Intrusion detection system: A comprehensive review","volume":"36","author":"Liao","year":"2013","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439950","article-title":"Deep learning for anomaly detection: A review","volume":"54","author":"Pang","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.3844\/jcssp.2020.1546.1557","article-title":"A survey of methods for managing the classification and solution of data imbalance problem","volume":"16","author":"Hasib","year":"2020","journal-title":"J. Comput. Sci."},{"key":"ref_7","unstructured":"Fischer, A., and Igel, C. (2012, January 3\u20136). An introduction to restricted Boltzmann machines. Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 17th Iberoamerican Congress, CIARP 2012, Buenos Aires, Argentina."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5947","DOI":"10.4249\/scholarpedia.5947","article-title":"Deep belief networks","volume":"4","author":"Hinton","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xiao, C., Gou, Z., Tai, W., Zhang, K., and Zhou, F. (2023, January 4). Imputation-based time-series anomaly detection with conditional weight-incremental diffusion models. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA.","DOI":"10.1145\/3580305.3599391"},{"key":"ref_10","unstructured":"Xu, J., Wu, H., Wang, J., and Long, M. (2021). Anomaly transformer: Time series anomaly detection with association discrepancy. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zheng, K., Wu, C., and Yang, Y. (2019). Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors, 19.","DOI":"10.3390\/s19112528"},{"key":"ref_12","unstructured":"Radford, B.J., Apolonio, L.M., Trias, A.J., and Simpson, J.A. (2018). Network traffic anomaly detection using recurrent neural networks. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, J., Kim, H., Shim, M., and Choi, E. (2020). CNN-based network intrusion detection against denial-of-service attacks. Electronics, 9.","DOI":"10.3390\/electronics9060916"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.neucom.2018.05.027","article-title":"Ramp loss one-class support vector machine; a robust and effective approach to anomaly detection problems","volume":"310","author":"Tian","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108346","DOI":"10.1109\/ACCESS.2020.3001350","article-title":"Anomaly-based intrusion detection from network flow features using variational autoencoder","volume":"8","author":"Zavarak","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","unstructured":"Kim, G., Yi, H., Lee, J., Paek, Y., and Yoon, S. (2016). LSTM-based system-call language modeling and robust ensemble method for designing host-based intrusion detection systems. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.comnet.2018.07.025","article-title":"An evaluation of the performance of Restricted Boltzmann Machines as a model for anomaly network intrusion detection","volume":"144","author":"Aldwairi","year":"2018","journal-title":"Comput. Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e8001","DOI":"10.1002\/cpe.8001","article-title":"A feed forward deep neural network model using feature selection for cloud intrusion detection system","volume":"36","author":"Sharma","year":"2024","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13918","DOI":"10.1007\/s11227-024-05994-1","article-title":"Intrusion detection system: A deep neural network-based concatenated approach","volume":"80","author":"Sharma","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_20","unstructured":"Carreira-Perpinan, M.A., and Hinton, G. (2005, January 6\u20138). On contrastive divergence learning. Proceedings of the International Workshop on Artificial Intelligence and Statistics, Bridgetown, Barbados. Available online: https:\/\/proceedings.mlr.press\/r5\/carreira-perpinan05a.html."},{"key":"ref_21","unstructured":"Berglund, M., and Raiko, T. (2013). Stochastic gradient estimate variance in contrastive divergence and persistent contrastive divergence. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tieleman, T., and Hinton, G. (2009, January 14\u201318). Using fast weights to improve persistent contrastive divergence. Proceedings of the 26th Annual International Conference on Machine Learning Held in Conjunction with the 2007 International Conference on Inductive Logic Programming, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553506"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015, January 10\u201312). NSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, ACT, Australia.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"132911","DOI":"10.1109\/ACCESS.2020.3009843","article-title":"CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection","volume":"8","author":"Stiawan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Aygun, R.C., and Yavuz, A.G. (2017, January 26\u201328). Network anomaly detection with stochastically improved autoencoder based models. Proceedings of the 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), New York, NY, USA.","DOI":"10.1109\/CSCloud.2017.39"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1109\/TII.2020.3022432","article-title":"Variational LSTM enhanced anomaly detection for industrial big data","volume":"17","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104695","DOI":"10.1109\/ACCESS.2021.3100087","article-title":"Network anomaly detection using memory-augmented deep autoencoder","volume":"9","author":"Min","year":"2021","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., and Ghogho, M. (2016, January 26\u201329). Deep learning approach for network intrusion detection in software defined networking. Proceedings of the 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco.","DOI":"10.1109\/WINCOM.2016.7777224"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","article-title":"A deep learning approach for intrusion detection using recurrent neural networks","volume":"5","author":"Yin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.procs.2015.07.490","article-title":"Analysis of KDD dataset attributes-class wise for intrusion detection","volume":"57","author":"Aggarwal","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"48231","DOI":"10.1109\/ACCESS.2018.2863036","article-title":"Enhanced network anomaly detection based on deep neural networks","volume":"6","author":"Naseer","year":"2018","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Khan, F.A., and Gumaei, A. (2019, January 26\u201328). A comparative study of machine learning classifiers for network intrusion detection. Proceedings of the Artificial Intelligence and Security: 5th International Conference, ICAIS 2019, New York, NY, USA.","DOI":"10.1007\/978-3-030-24265-7_7"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"140806","DOI":"10.1109\/ACCESS.2019.2943249","article-title":"An empirical evaluation of deep learning for network anomaly detection","volume":"7","author":"Malaiya","year":"2019","journal-title":"IEEE Access"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/9\/1220\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:57:56Z","timestamp":1760111876000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/9\/1220"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,17]]},"references-count":33,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["sym16091220"],"URL":"https:\/\/doi.org\/10.3390\/sym16091220","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,17]]}}}