{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T21:32:46Z","timestamp":1775511166039,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031578694","type":"print"},{"value":"9783031578700","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-57870-0_29","type":"book-chapter","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T04:01:52Z","timestamp":1712635312000},"page":"326-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Intrusion Detection System Using Machine Learning and\u00a0Deep Learning"],"prefix":"10.1007","author":[{"given":"R.","family":"Madhusudhan","sequence":"first","affiliation":[]},{"given":"Shubham Kumar","family":"Thakur","sequence":"additional","affiliation":[]},{"given":"P.","family":"Pravisha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"key":"29_CR1","unstructured":"Li, Y., Zhang, L., Cao, J.: A survey of intrusion detection systems based on machine learning and deep learning in the IoT environment. J. Ambient Intell. Humaniz. Comput. (2021)"},{"key":"29_CR2","unstructured":"Haddadouche, N., Benameur, A., Mokdad, L., Alajlan, N.: Deep neural networks for intrusion detection: a comprehensive review. Int. J. Netw. Manag. (2020)"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Zhong, M., Zhou, Y., Chen, G.: Sequential model-based intrusion detection system for IoT servers using deep learning methods. Sensors 21(4), 1113.48 (2021)","DOI":"10.3390\/s21041113"},{"key":"29_CR4","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1089\/big.2020.0263","volume":"9","author":"I Al-Turaiki","year":"2021","unstructured":"Al-Turaiki, I., Altwaijry, N.: A convolutional neural network for improved anomaly-based network intrusion detection. Big Data 9, 233\u2013252 (2021)","journal-title":"Big Data"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Azzaoui, H., Boukhamla, A.Z.E., Arroyo, D., Abdallah, B.: Developing new deep-learning model to enhance network intrusion classification. Evol. Syst. (2022)","DOI":"10.1007\/s12530-020-09364-z"},{"issue":"16","key":"29_CR6","doi-asserted-by":"publisher","first-page":"7986","DOI":"10.3390\/app12167986","volume":"12","author":"EUH Qazi","year":"2022","unstructured":"Qazi, E.U.H., Almorjan, A., Zia, T.: A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection. Appl. Sci. 12(16), 7986 (2022)","journal-title":"Appl. Sci."},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi, L., Zhang, J., Humaidi, A.J., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 1\u201374 (2021)","journal-title":"J. Big Data"},{"issue":"6","key":"29_CR8","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., Li, Q., Xiang, W.: A deep learning model for network intrusion detection with imbalanced data. Electronics 11(6), 898 (2022)","journal-title":"Electronics"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Ashiku, L., Dagli, C.: Network intrusion detection system using deep learning. Procedia Comput. Sci. 185, 239\u2013247 (2021). ISSN 1877-0509","DOI":"10.1016\/j.procs.2021.05.025"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Vaiyapuri, T., Sbai, Z., Alaskar, H., Alaseem, N.A.: Deep learning approaches for intrusion detection in IIoT networks-opportunities and future directions. Int. J. Adv. Comput. Sci. Appl. 12(4) (2021)","DOI":"10.14569\/IJACSA.2021.0120411"},{"key":"29_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107315","volume":"177","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Huang, L., Wu, C.Q., Li, Z.: An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset. Comput. Netw. 177, 107315 (2020)","journal-title":"Comput. Netw."},{"key":"29_CR12","doi-asserted-by":"publisher","first-page":"9731","DOI":"10.1007\/s00500-021-05893-0","volume":"25","author":"G Kocher","year":"2021","unstructured":"Kocher, G., Kumar, G.: Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges. Soft Comput. 25, 9731\u20139763 (2021)","journal-title":"Soft Comput."},{"key":"29_CR13","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1162\/COLI_r_00312","volume":"44","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Zhang, M.: Neural network methods for natural language processing. Comput. Linguist. 44, 193\u2013195 (2018)","journal-title":"Comput. Linguist."},{"issue":"3","key":"29_CR14","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1109\/TNET.2021.3137084","volume":"30","author":"C Zhang","year":"2022","unstructured":"Zhang, C., Costa-Perez, X., Patras, P.: Adversarial attacks against deep learning-based network intrusion detection systems and defense mechanisms. IEEE\/ACM Trans. Netw. 30(3), 1294\u20131311 (2022)","journal-title":"IEEE\/ACM Trans. Netw."},{"issue":"13","key":"29_CR15","doi-asserted-by":"publisher","first-page":"7507","DOI":"10.3390\/app13137507","volume":"13","author":"P Dini","year":"2023","unstructured":"Dini, P., Elhanashi, A., Begni, A., Saponara, S., Zheng, Q., Gasmi, K.: Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity. Appl. Sci. 13(13), 7507 (2023). https:\/\/doi.org\/10.3390\/app13137507","journal-title":"Appl. Sci."},{"key":"29_CR16","first-page":"7","volume":"1","author":"V Kumar","year":"2012","unstructured":"Kumar, V.: Signature based intrusion detection system using SNORT. Int. J. Comput. Appl. Inf. Technol. 1, 7 (2012)","journal-title":"Int. J. Comput. Appl. Inf. Technol."},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Lee, S.W., Mohammadi, M., Rashidi, S., Rahmani, A.M., Masdari, M., Hosseinzadeh, M.: Towards secure intrusion detection systems using deep learning techniques: comprehensive analysis and review. J. Netw. Comput. Appl. 187, 103111 (2021). ISSN 1084-8045","DOI":"10.1016\/j.jnca.2021.103111"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Advanced Information Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-57870-0_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T04:10:59Z","timestamp":1712635859000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-57870-0_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031578694","9783031578700"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-57870-0_29","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"value":"2367-4512","type":"print"},{"value":"2367-4520","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"10 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AINA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Information Networking and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"38","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aina2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/aina\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}