{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:27:27Z","timestamp":1774718847457,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T00:00:00Z","timestamp":1611273600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T00:00:00Z","timestamp":1611273600000},"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":["Cluster Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10586-021-03231-5","type":"journal-article","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T15:27:47Z","timestamp":1611329267000},"page":"2489-2502","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Towards the design of real-time autonomous IoT NIDS"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2825-2448","authenticated-orcid":false,"given":"Alaa","family":"Alhowaide","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Izzat","family":"Alsmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,22]]},"reference":[{"key":"3231_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-020-03137-8","author":"O Alfandi","year":"2020","unstructured":"Alfandi, O., Khanji, S., Ahmad, L., Khattak, A.: A survey on boosting IoT security and privacy through blockchain. Clust. Comput. (2020). https:\/\/doi.org\/10.1007\/s10586-020-03137-8","journal-title":"Clust. Comput."},{"key":"3231_CR2","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. ArXiv180209089 Cs, 2018. Available: http:\/\/arxiv.org\/abs\/1802.09089 (accessed 24 Oct 2019)","DOI":"10.14722\/ndss.2018.23204"},{"issue":"1","key":"3231_CR3","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s10586-018-2516-1","volume":"22","author":"D Li","year":"2019","unstructured":"Li, D., Cai, Z., Deng, L., Yao, X., Wang, H.H.: Information security model of block chain based on intrusion sensing in the IoT environment. Clust. Comput. 22(1), 451\u2013468 (2019). https:\/\/doi.org\/10.1007\/s10586-018-2516-1","journal-title":"Clust. Comput."},{"issue":"4","key":"3231_CR4","doi-asserted-by":"publisher","first-page":"2609","DOI":"10.1007\/s10586-019-03031-y","volume":"23","author":"S Mahdavi Hezavehi","year":"2020","unstructured":"Mahdavi Hezavehi, S., Rahmani, R.: An anomaly-based framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments. Clust. Comput. 23(4), 2609\u20132627 (2020). https:\/\/doi.org\/10.1007\/s10586-019-03031-y","journal-title":"Clust. Comput."},{"key":"3231_CR5","doi-asserted-by":"publisher","unstructured":"Mohamed, T. Otsuka, T., Ito, T.: Towards machine learning based IoT intrusion detection service. In: Recent Trends Future Technol. Appl. Intell. IEAAIE 2018 Lect. Notes Comput. Sci., vol. 10868 (2018). https:\/\/doi.org\/10.1007\/978-3-319-92058-0_56","DOI":"10.1007\/978-3-319-92058-0_56"},{"key":"3231_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cose.2014.04.009","volume":"45","author":"A Shameli-Sendi","year":"2014","unstructured":"Shameli-Sendi, A., Cheriet, M., Hamou-Lhaj, A.: Taxonomy of intrusion risk assessment and response system | Elsevier Enhanced Reader. Comput. Secur. 45, 1\u201316 (2014). https:\/\/doi.org\/10.1016\/j.cose.2014.04.009","journal-title":"Comput. Secur."},{"key":"3231_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-72119-4","volume-title":"Practical Information Security: A Competency-Based Education Course","author":"I Alsmadi","year":"2018","unstructured":"Alsmadi, I., Burdwell, R., Aleroud, A., Wahbeh, A., Qudah, M., Al-Omari, A.: Practical Information Security: A Competency-Based Education Course. Springer, New York (2018)"},{"key":"3231_CR8","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jnca.2018.12.006","volume":"128","author":"N Moustafa","year":"2019","unstructured":"Moustafa, N., Hu, J., Slay, J.: A holistic review of Network Anomaly Detection Systems: a comprehensive survey | Elsevier Enhanced Reader. J. Netw. Comput. Appl. 128, 33\u201355 (2019). https:\/\/doi.org\/10.1016\/j.jnca.2018.12.006","journal-title":"J. Netw. Comput. Appl."},{"issue":"6","key":"3231_CR9","doi-asserted-by":"publisher","first-page":"13405","DOI":"10.1007\/s10586-018-1944-2","volume":"22","author":"A Guo","year":"2019","unstructured":"Guo, A., Xu, M., Ran, F., Wang, H.: A novel medical internet of things perception system based on visual image encryption and intrusion detection. Clust. Comput. 22(6), 13405\u201313413 (2019). https:\/\/doi.org\/10.1007\/s10586-018-1944-2","journal-title":"Clust. Comput."},{"issue":"2","key":"3231_CR10","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.1007\/s10586-017-0841-4","volume":"20","author":"D-Y Kim","year":"2017","unstructured":"Kim, D.-Y., Kim, S., Hassan, H., Park, J.H.: Radio resource management for data transmission in low power wide area networks integrated with large scale cyber physical systems. Clust. Comput. 20(2), 1831\u20131842 (2017). https:\/\/doi.org\/10.1007\/s10586-017-0841-4","journal-title":"Clust. Comput."},{"issue":"4","key":"3231_CR11","doi-asserted-by":"publisher","first-page":"9889","DOI":"10.1007\/s10586-018-1847-2","volume":"22","author":"L Deng","year":"2019","unstructured":"Deng, L., Li, D., Yao, X., Cox, D., Wang, H.: Mobile network intrusion detection for IoT system based on transfer learning algorithm. Clust. Comput. 22(4), 9889\u20139904 (2019). https:\/\/doi.org\/10.1007\/s10586-018-1847-2","journal-title":"Clust. Comput."},{"key":"3231_CR12","doi-asserted-by":"publisher","first-page":"94497","DOI":"10.1109\/ACCESS.2019.2928048","volume":"7","author":"BA Tama","year":"2019","unstructured":"Tama, B.A., Comuzzi, M., Rhee, K.-H.: TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE Access 7, 94497\u201394507 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2928048","journal-title":"IEEE Access"},{"key":"3231_CR13","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.1954824","author":"C Okoli","year":"2010","unstructured":"Okoli, C., Schabram, K.: A guide to conducting a systematic literature review of information systems research. SSRN Electron. J. (2010). https:\/\/doi.org\/10.2139\/ssrn.1954824","journal-title":"SSRN Electron. J."},{"issue":"3","key":"3231_CR14","doi-asserted-by":"publisher","first-page":"4815","DOI":"10.1109\/JIOT.2018.2871719","volume":"6","author":"N Moustafa","year":"2019","unstructured":"Moustafa, N., Turnbull, B., Choo, K.-K.R.: An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of Internet of Things. IEEE Internet Things J. 6(3), 4815\u20134830 (2019). https:\/\/doi.org\/10.1109\/JIOT.2018.2871719","journal-title":"IEEE Internet Things J."},{"key":"3231_CR15","doi-asserted-by":"publisher","unstructured":"Pham, N.T., Foo, E., Suriadi, S., Jeffrey, H., Lahza, H.F.M.: Improving performance of intrusion detection system using ensemble methods and feature selection. In: Proceedings of the Australasian Computer Science Week Multiconference on\u2014ACSW '18, Brisband, Queensland, Australia, 2018, pp. 1\u20136. https:\/\/doi.org\/10.1145\/3167918.3167951","DOI":"10.1145\/3167918.3167951"},{"issue":"1","key":"3231_CR16","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s10586-015-0527-8","volume":"19","author":"S-H Kang","year":"2016","unstructured":"Kang, S.-H., Kim, K.J.: A feature selection approach to find optimal feature subsets for the network intrusion detection system. Clust. Comput. 19(1), 325\u2013333 (2016). https:\/\/doi.org\/10.1007\/s10586-015-0527-8","journal-title":"Clust. Comput."},{"key":"3231_CR17","unstructured":"Radford, B.J., Richardson, B.D., Davis, S.E.: Sequence aggregation rules for anomaly detection in computer network traffic. ArXiv Prepr. ArXiv180503735, p. 13, 2018."},{"key":"3231_CR18","unstructured":"\"NSL-KDD | Datasets | Research | Canadian Institute for Cybersecurity | UNB.\" https:\/\/www.unb.ca\/cic\/datasets\/nsl.html (accessed Nov. 20, 2019)"},{"issue":"3","key":"3231_CR19","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan, Y., et al.: N-BaIoT\u2014network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12\u201322 (2018). https:\/\/doi.org\/10.1109\/MPRV.2018.03367731","journal-title":"IEEE Pervasive Comput."},{"key":"3231_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jisa.2018.05.002","volume":"41","author":"M Al-Hawawreh","year":"2018","unstructured":"Al-Hawawreh, M., Moustafa, N., Sitnikova, E.: Identification of malicious activities in industrial internet of things based on deep learning models. J. Inf. Secur. Appl. 41, 1\u201311 (2018). https:\/\/doi.org\/10.1016\/j.jisa.2018.05.002","journal-title":"J. Inf. Secur. Appl."},{"key":"3231_CR21","doi-asserted-by":"publisher","unstructured":"Moustafa, N., Slay, J.: 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), 2015, pp. 1\u20136, https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"3231_CR22","doi-asserted-by":"publisher","unstructured":"Verma, A., Ranga, V.: ELNIDS: Ensemble Learning Based Network Intrusion Detection System for RPL based Internet of Things. In: Proceedings of the 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), 2019, pp. 1\u20136. https:\/\/doi.org\/10.1109\/IoT-SIU.2019.8777504","DOI":"10.1109\/IoT-SIU.2019.8777504"},{"key":"3231_CR23","doi-asserted-by":"publisher","unstructured":"Verma, A., Ranga, V.: RPL-NIDDS17\u2014a data set for Intrusion Detection in RPL based 6LoWPAN Networks (Internet of Things). https:\/\/doi.org\/10.5281\/zenodo.1406034","DOI":"10.5281\/zenodo.1406034"},{"issue":"2","key":"3231_CR24","doi-asserted-by":"publisher","first-page":"4065","DOI":"10.1007\/s10586-018-2686-x","volume":"22","author":"S Vimala","year":"2019","unstructured":"Vimala, S., Khanaa, V., Nalini, C.: A study on supervised machine learning algorithm to improvise intrusion detection systems for mobile ad hoc networks. Clust. Comput. 22(2), 4065\u20134074 (2019). https:\/\/doi.org\/10.1007\/s10586-018-2686-x","journal-title":"Clust. Comput."},{"issue":"6","key":"3231_CR25","doi-asserted-by":"publisher","first-page":"13027","DOI":"10.1007\/s10586-017-1187-7","volume":"22","author":"V Balamurugan","year":"2019","unstructured":"Balamurugan, V., Saravanan, R.: Enhanced intrusion detection and prevention system on cloud environment using hybrid classification and OTS generation. Clust. Comput. 22(6), 13027\u201313039 (2019). https:\/\/doi.org\/10.1007\/s10586-017-1187-7","journal-title":"Clust. Comput."},{"key":"3231_CR26","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.protcy.2012.05.017","volume":"4","author":"S Mukherjee","year":"2012","unstructured":"Mukherjee, S., Sharma, N.: Intrusion detection using Naive bayes classifier with feature reduction | Elsevier enhanced reader. Procedia Technol. 4, 119\u2013128 (2012). https:\/\/doi.org\/10.1016\/j.protcy.2012.05.017","journal-title":"Procedia Technol."},{"key":"3231_CR27","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2011","unstructured":"Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, New York (2011)"},{"issue":"5","key":"3231_CR28","doi-asserted-by":"publisher","first-page":"10549","DOI":"10.1007\/s10586-017-1109-8","volume":"22","author":"S Aljawarneh","year":"2019","unstructured":"Aljawarneh, S., Yassein, M.B., Aljundi, M.: An enhanced J48 classification algorithm for the anomaly intrusion detection systems. Clust. Comput. 22(5), 10549\u201310565 (2019). https:\/\/doi.org\/10.1007\/s10586-017-1109-8","journal-title":"Clust. Comput."},{"key":"3231_CR29","doi-asserted-by":"publisher","unstructured":"Miller, S.T., Busby-Earle, C.: Multi-perspective machine learning a classifier ensemble method for intrusion detection. In: Proceedings of the 2017 International Conference on Machine Learning and Soft Computing\u2014ICMLSC '17, Ho Chi Minh City, Vietnam, 2017, pp. 7\u201312. https:\/\/doi.org\/10.1145\/3036290.3036303","DOI":"10.1145\/3036290.3036303"},{"issue":"6","key":"3231_CR30","doi-asserted-by":"publisher","first-page":"14721","DOI":"10.1007\/s10586-018-2385-7","volume":"22","author":"L Gao","year":"2019","unstructured":"Gao, L., Li, F., Xu, X., Liu, Y.: Intrusion detection system using SOEKS and deep learning for in-vehicle security. Clust. Comput. 22(6), 14721\u201314729 (2019). https:\/\/doi.org\/10.1007\/s10586-018-2385-7","journal-title":"Clust. Comput."},{"key":"3231_CR31","unstructured":"UCI Machine Learning Repository: detection_of_IoT_botnet_attacks_N_BaIoT Data Set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/detection_of_IoT_botnet_attacks_N_BaIoT (accessed 27 Nov 2019)"},{"key":"3231_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-020-03153-8","author":"AJ Siddiqui","year":"2020","unstructured":"Siddiqui, A.J., Boukerche, A.: TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of Things. Clust. Comput. (2020). https:\/\/doi.org\/10.1007\/s10586-020-03153-8","journal-title":"Clust. Comput."},{"key":"3231_CR33","doi-asserted-by":"publisher","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1\u20136. https:\/\/doi.org\/10.1109\/CISDA.2009.5356528","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"5","key":"3231_CR34","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1002\/sec.800","volume":"7","author":"S Garc\u00eda","year":"2014","unstructured":"Garc\u00eda, S., Zunino, A., Campo, M.: Survey on network-based botnet detection methods. Secur. Commun. Netw. 7(5), 878\u2013903 (2014). https:\/\/doi.org\/10.1002\/sec.800","journal-title":"Secur. Commun. Netw."},{"key":"3231_CR35","doi-asserted-by":"crossref","unstructured":"Aldwairi, M., Mardini, W., Alhowaide, A.: Anomaly payload signature generation system based on efficient tokenization methodology. In: Int. J. Commun. Antenna Propag. IRECAP 2018, 2018.","DOI":"10.15866\/irecap.v8i5.12794"},{"key":"3231_CR36","unstructured":"Figures\/PerformanceMeasuresFigures.pdf master Alaa Alhowaide \/ Towards the Design of Real-Time Autonomous IoT NIDS. GitLab. https:\/\/gitlab.com\/azalhowaide\/towards-the-design-of-real-time-autonomous-iot-nids\/-\/blob\/master\/Figures\/PerformanceMeasuresFigures.pdf (accessed 3 Mar 2020)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-021-03231-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-021-03231-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-021-03231-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T20:20:15Z","timestamp":1693081215000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-021-03231-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,22]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["3231"],"URL":"https:\/\/doi.org\/10.1007\/s10586-021-03231-5","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,22]]},"assertion":[{"value":"10 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}