{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T07:39:17Z","timestamp":1774337957815,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031557286","type":"print"},{"value":"9783031557293","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\/978-3-031-55729-3_19","type":"book-chapter","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T10:50:56Z","timestamp":1710931856000},"page":"237-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Survey on\u00a0Intrusion Detection Systems for\u00a0IoT Networks Based on\u00a0Long Short-Term Memory"],"prefix":"10.1007","author":[{"given":"Nour Elhouda","family":"Oueslati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hichem","family":"Mrabet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abderrazak","family":"Jemai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"19_CR1","unstructured":"Ghumro, A., Memon, A.K., Memon, I., Simming, I.A.: A review of mitigation of attacks in IoT using deep learning models. Quaid-E-Awam Univ. Res. J. Eng. Sci. Technol. Nawabshah. 18(1), 36\u201342 (2020)"},{"issue":"3","key":"19_CR2","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1109\/COMST.2021.3086296","volume":"23","author":"E Rodriguez","year":"2021","unstructured":"Rodriguez, E., Otero, B., Gutierrez, N., Canal, R.: A survey of deep learning techniques for cybersecurity in mobile networks. IEEE Commun. Surv. Tutorials 23(3), 1920\u20131955 (2021)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"19_CR3","first-page":"101322","volume":"38","author":"HC Altunay","year":"2023","unstructured":"Altunay, H.C., Albayrak, Z.: A hybrid CNN+ LSTMbased intrusion detection system for industrial IoT networks. Eng. Sci. Technol. Int. J. 38, 101322 (2023)","journal-title":"Eng. Sci. Technol. Int. J."},{"issue":"4","key":"19_CR4","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.3390\/s23042171","volume":"23","author":"YC Wang","year":"2023","unstructured":"Wang, Y.C., Houng, Y.C., Chen, H.X., Tseng, S.M.: Network anomaly intrusion detection based on deep learning approach. Sensors 23(4), 2171 (2023)","journal-title":"Sensors"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Alabadi, M., Celik, Y.: Anomaly detection for cyber-security based on convolution neural network: A survey. In: International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1\u201314. IEEE (2020)","DOI":"10.1109\/HORA49412.2020.9152899"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Al Razib, M., Javeed, D., Khan, M.T., Alkanhel, R., Muthanna, M.S.A.: Cyber threats detection in smart environments using SDN-enabled DNN-LSTM hybrid framework. IEEE Access, 10, 53015\u201353026 (2022)","DOI":"10.1109\/ACCESS.2022.3172304"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Wahab, F., et al.: An AI-driven hybrid framework for intrusion detection in IoT-enabled E-health. Comput. Intell. Neurosci. (2022)","DOI":"10.1155\/2022\/6096289"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Roopak, M., Tian, G.Y., Chambers, J.: Deep learning models for cyber security in IoT networks. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0452\u20130457 (2019)","DOI":"10.1109\/CCWC.2019.8666588"},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"107139","DOI":"10.1016\/j.measurement.2019.107139","volume":"152","author":"W Niu","year":"2020","unstructured":"Niu, W., Zhang, X., Du, X., Zhao, L., Cao, R., Guizani, M.: A deep learning based static taint analysis approach for IoT software vulnerability location. Measurement 152, 107139 (2020)","journal-title":"Measurement"},{"issue":"6","key":"19_CR10","doi-asserted-by":"publisher","first-page":"4944","DOI":"10.1109\/JIOT.2020.3034156","volume":"8","author":"SI Popoola","year":"2020","unstructured":"Popoola, S.I., Adebisi, B., Hammoudeh, M., Gui, G., Gacanin, H.: Hybrid deep learning for botnet attack detection in the internet-of-things networks. IEEE Internet Things J. 8(6), 4944\u20134956 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"19_CR11","first-page":"2701","volume":"10","author":"S Laqtib","year":"2020","unstructured":"Laqtib, S., El Yassini, K., Hasnaoui, M.L.: A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET. Int. J. Electr. Comput. Eng. 10(3), 2701 (2020)","journal-title":"Int. J. Electr. Comput. Eng."},{"issue":"3","key":"19_CR12","first-page":"8","volume":"3","author":"B Andreas","year":"2020","unstructured":"Andreas, B., Dilruksha, J., McCandless, E.: Flow-based and packet-based intrusion detection using BLSTM. SMU Data Sc. Rev. 3(3), 8 (2020)","journal-title":"SMU Data Sc. Rev."},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Mao, Y., Li, Y., Sun, J., Chen, Y.: Explainable software vulnerability detection based on attention-based bidirectional recurrent neural networks. In IEEE International Conference on Big Data (Big Data), pp. 4651\u20134656. IEEE (2020)","DOI":"10.1109\/BigData50022.2020.9377803"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Saurabh, K., et al.: LBDMIDS: LSTM based deep learning model for intrusion detection systems for IoT networks. In IEEE World AI IoT Congress (AIIoT), pp. 753\u2013759. IEEE (2022)","DOI":"10.1109\/AIIoT54504.2022.9817245"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Li, W., Chang, C.: IoT intrusion detection system based on LSTM model. In 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE), pp. 1404\u20131409. Atlantis Press (2022)","DOI":"10.2991\/978-94-6463-040-4_209"},{"issue":"3s2","key":"19_CR16","first-page":"1375","volume":"71","author":"MV Gaur","year":"2022","unstructured":"Gaur, M.V., Kumar, R.: M-LSTM: multiclass long short-term memory based approach for detection of DDoS attacks. Math. Stat. Eng. Appl. 71(3s2), 1375\u20131394 (2022)","journal-title":"Math. Stat. Eng. Appl."},{"issue":"1","key":"19_CR17","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3390\/s22010185","volume":"22","author":"M Al-Sarem","year":"2022","unstructured":"Al-Sarem, M., Saeed, F., Alkhammash, E.H., Alghamdi, N.S.: An aggregated mutual information based feature selection with machine learning methods for enhancing IoT botnet attack detection. Sensors 22(1), 185 (2022)","journal-title":"Sensors"},{"issue":"19","key":"19_CR18","doi-asserted-by":"publisher","first-page":"6432","DOI":"10.3390\/s21196432","volume":"21","author":"K Albulayhi","year":"2021","unstructured":"Albulayhi, K., Smadi, A.A., Sheldon, F.T., Abercrombie, R.K.: IoT intrusion detection taxonomy, reference architecture, and analyses. Sensors 21(19), 6432 (2021)","journal-title":"Sensors"},{"issue":"1","key":"19_CR19","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"FJ Ord\u00f3\u00f1ez","year":"2016","unstructured":"Ord\u00f3\u00f1ez, F.J., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)","journal-title":"Sensors"},{"issue":"9","key":"19_CR20","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.3390\/electronics11091502","volume":"11","author":"U Inayat","year":"2022","unstructured":"Inayat, U., Zia, M.F., Mahmood, S., Khalid, H.M., Benbouzid, M.: Learning-based methods for cyber attacks detection in IoT systems: a survey on methods, analysis, and future prospects. Electronics 11(9), 1502 (2022)","journal-title":"Electronics"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, J., Thu, H.L.T., Kim, H.: Long short term memory recurrent neural network classifier for intrusion detection. In: International Conference on Platform Technology and Service (PlatCon), pp. 1\u20135. IEEE (2016)","DOI":"10.1109\/PlatCon.2016.7456805"},{"issue":"1","key":"19_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3390\/jsan12010003","volume":"12","author":"R Elsayed","year":"2022","unstructured":"Elsayed, R., Hamada, R., Hammoudeh, M., Abdalla, M., Elsaid, S.A.: A hierarchical deep learning-based intrusion detection architecture for clustered internet of things. J. Sensor Actuator Netw. 12(1), 3 (2022)","journal-title":"J. Sensor Actuator Netw."},{"issue":"24","key":"19_CR23","doi-asserted-by":"publisher","first-page":"4147","DOI":"10.3390\/electronics11244147","volume":"11","author":"AR Khan","year":"2022","unstructured":"Khan, A.R., Yasin, A., Usman, S.M., Hussain, S., Khalid, S., Ullah, S.S.: Exploring lightweight deep learning solution for malware detection in IoT constraint environment. Electronics 11(24), 4147 (2022)","journal-title":"Electronics"},{"key":"19_CR24","unstructured":"Amit, I., Matherly, J., Hewlett, W., Xu, Z., Meshi, Y., Weinberger, Y.: Machine learning in cyber-security-problems, challenges and data sets. In: arXiv preprint arXiv:1812.07858 (2018)"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Dua, S., Du, X.: Data mining and machine learning in cybersecurity. In: CRC Press (2016)","DOI":"10.1201\/b10867"},{"key":"19_CR26","doi-asserted-by":"publisher","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","volume":"6","author":"Y Xin","year":"2018","unstructured":"Xin, Y., et al.: Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365\u201335381 (2018)","journal-title":"IEEE Access"},{"key":"19_CR27","doi-asserted-by":"publisher","first-page":"105124","DOI":"10.1016\/j.knosys.2019.105124","volume":"189","author":"A Aldweesh","year":"2020","unstructured":"Aldweesh, A., Derhab, A., Emam, A.Z.: Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues. Knowl. Based Syst. 189, 105124 (2020)","journal-title":"Knowl. Based Syst."},{"issue":"5","key":"19_CR28","doi-asserted-by":"publisher","first-page":"909","DOI":"10.3390\/app9050909","volume":"9","author":"S Qiu","year":"2019","unstructured":"Qiu, S., Liu, Q., Zhou, S., Wu, C.: Review of artificial intelligence adversarial attack and defense technologies. Appl. Sci. 9(5), 909 (2019)","journal-title":"Appl. Sci."},{"key":"19_CR29","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-642-40597-6_15","volume-title":"Advances in Security of Information and Communication Networks","author":"A Alazab","year":"2013","unstructured":"Alazab, A., Hobbs, M., Abawajy, J., Khraisat, A.: Developing an intelligent intrusion detection and prevention system against web application malware. In: Awad, A.I., Hassanien, A.E., Baba, K. (eds.) SecNet 2013. CCIS, vol. 381, pp. 177\u2013184. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40597-6_15"},{"key":"19_CR30","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-030-04503-6_14","volume-title":"Trends and Applications in Knowledge Discovery and Data Mining","author":"A Khraisat","year":"2018","unstructured":"Khraisat, A., Gondal, I., Vamplew, P.: An anomaly intrusion detection system using C5 decision tree classifier. In: Ganji, M., Rashidi, L., Fung, B.C.M., Wang, C. (eds.) PAKDD 2018. LNCS (LNAI), vol. 11154, pp. 149\u2013155. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04503-6_14"},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Ghazal, S.F., Mjlae, S.A.: Cybersecurity in deep learning techniques: detecting network attacks. Int. J. Adv. Comput. Sci. Appl. 13(11) (2022)","DOI":"10.14569\/IJACSA.2022.0131125"},{"issue":"1","key":"19_CR32","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s42400-022-00133-w","volume":"6","author":"R Alghamdi","year":"2023","unstructured":"Alghamdi, R., Bellaiche, M.: An ensemble deep learning based IDS for IoT using Lambda architecture. Cybersecurity 6(1), 5 (2023)","journal-title":"Cybersecurity"},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Peterson, J.M., Leevy, J.L., Khoshgoftaar, T.M.: A review and analysis of the Bot-IoT dataset. In: IEEE International Conference on Service-Oriented System Engineering (SOSE), pp. 20\u201327 (2021)","DOI":"10.1109\/SOSE52839.2021.00007"},{"key":"19_CR34","doi-asserted-by":"crossref","unstructured":"Panwar, S.S., Negi, P.S., Panwar, L.S., Raiwani, Y.P.: Implementation of machine learning algorithms on CICIDS-2017 dataset for intrusion detection using WEKA. Int. J. Recent Technol. Eng. Regular Issue, 8(3), 2195\u20132207 (2019)","DOI":"10.35940\/ijrte.C4587.098319"},{"key":"19_CR35","unstructured":"Zoghi, Z. and Serpen, G.: UNSW-NB15 computer security dataset: analysis through visualization. In: arXiv preprint arXiv:2101.05067, (2021)"},{"key":"19_CR36","unstructured":"Stoian, N.A.: Machine learning for anomaly detection in iot networks: Malware analysis on the IoT-23 data set (Bachelor\u2019s thesis, University of Twente) (2020)"}],"container-title":["Communications in Computer and Information Science","Advances in Model and Data Engineering in the Digitalization Era"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-55729-3_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T04:36:23Z","timestamp":1745555783000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-55729-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031557286","9783031557293"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-55729-3_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MEDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Model and Data Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sousse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tunisia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"medi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/medi2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}