{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:33:45Z","timestamp":1785422025260,"version":"3.56.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"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":[[2023,6]]},"DOI":"10.1007\/s10586-022-03719-8","type":"journal-article","created":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T19:02:46Z","timestamp":1661454166000},"page":"1801-1819","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Metaheuristic feature selection with deep learning enabled cascaded recurrent neural network for anomaly detection in Industrial Internet of Things environment"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7889-6511","authenticated-orcid":false,"given":"Nenavath","family":"Chander","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mummadi","family":"Upendra Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"3719_CR1","doi-asserted-by":"publisher","first-page":"74217","DOI":"10.1109\/ACCESS.2019.2920699","volume":"7","author":"B Genge","year":"2019","unstructured":"Genge, B., Haller, P., En\u0103chescu, C.: Anomaly detection in aging industrial internet of things. IEEE Access 7, 74217\u201374230 (2019)","journal-title":"IEEE Access"},{"issue":"7","key":"3719_CR2","doi-asserted-by":"publisher","first-page":"2376","DOI":"10.3390\/s21072376","volume":"21","author":"P Tanuska","year":"2021","unstructured":"Tanuska, P., Spendla, L., Kebisek, M., Duris, R., Stremy, M.: Smart anomaly detection and prediction for assembly process maintenance in compliance with industry 4.0. Sensors 21(7), 2376 (2021)","journal-title":"Sensors"},{"key":"3719_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103509","volume":"132","author":"TT Huong","year":"2021","unstructured":"Huong, T.T., Bac, T.P., Long, D.M., Luong, T.D., Dan, N.M., Thang, B.D., Tran, K.P.: Detecting cyberattacks using anomaly detection in industrial control systems: a federated learning approach. Comput. Ind. 132, 103509 (2021)","journal-title":"Comput. Ind."},{"key":"3719_CR4","doi-asserted-by":"crossref","unstructured":"Bulla, C., Birje, M.N.: Anomaly detection in industrial IoT applications using deep learning approach. In: Fernandes, S.L., Sharma, T.K. (eds.) Artificial Intelligence in Industrial Applications, pp. 127\u2013147. Springer, Cham (2022)","DOI":"10.1007\/978-3-030-85383-9_9"},{"issue":"4","key":"3719_CR5","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1109\/JIOT.2021.3077803","volume":"9","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri, V., Khare, P., Parizi, R.M., Pouriyeh, S., Dehghantanha, A., Srivastava, G.: Federated learning-based anomaly detection for IoT security attacks. IEEE Internet Things J. 9(4), 2545\u20132554 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"6","key":"3719_CR6","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1007\/s10845-021-01768-1","volume":"32","author":"P Zhan","year":"2021","unstructured":"Zhan, P., Wang, S., Wang, J., Qu, L., Wang, K., Hu, Y., Li, X.: Temporal anomaly detection on IIoT-enabled manufacturing. J. Intell. Manuf. 32(6), 1669\u20131678 (2021)","journal-title":"J. Intell. Manuf."},{"issue":"7","key":"3719_CR7","doi-asserted-by":"publisher","first-page":"5219","DOI":"10.1109\/JIOT.2021.3051935","volume":"8","author":"G Han","year":"2021","unstructured":"Han, G., Tu, J., Liu, L., Mart\u00ednez-Garc\u00eda, M., Peng, Y.: Anomaly detection based on multidimensional data processing for protecting vital devices in 6G-enabled massive IIoT. IEEE Internet Things J. 8(7), 5219\u20135229 (2021)","journal-title":"IEEE Internet Things J."},{"key":"3719_CR8","doi-asserted-by":"crossref","unstructured":"Sharghivand, N., Derakhshan, F.: Classification and intelligent mining of anomalies in Industrial IoT. In: Karimipour, H., Derakhshan, F. (eds.) AI-Enabled Threat Detection and Security Analysis for Industrial IoT (pp. 163\u2013180). Springer, Cham (2021)","DOI":"10.1007\/978-3-030-76613-9_9"},{"key":"3719_CR9","doi-asserted-by":"publisher","first-page":"53370","DOI":"10.1109\/ACCESS.2021.3068756","volume":"9","author":"L Fu","year":"2021","unstructured":"Fu, L., Zhang, W., Tan, X., Zhu, H.: An algorithm for detection of traffic attribute exceptions based on cluster algorithm in industrial internet of things. IEEE Access 9, 53370\u201353378 (2021)","journal-title":"IEEE Access"},{"key":"3719_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108616","volume":"203","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Tian, J., Fang, H., Chen, L., Qin, J.: LightLog: a lightweight temporal convolutional network for log anomaly detection on the edge. Comput. Netw. 203, 108616 (2022)","journal-title":"Comput. Netw."},{"key":"3719_CR11","doi-asserted-by":"publisher","first-page":"111257","DOI":"10.1109\/ACCESS.2019.2930627","volume":"7","author":"Y Peng","year":"2019","unstructured":"Peng, Y., Tan, A., Wu, J., Bi, Y.: Hierarchical edge computing: a novel multi-source multi-dimensional data anomaly detection scheme for industrial Internet of Things. IEEE Access 7, 111257\u2013111270 (2019)","journal-title":"IEEE Access"},{"key":"3719_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3078192","author":"F Kong","year":"2021","unstructured":"Kong, F., Li, J., Jiang, B., Wang, H., Song, H.: Integrated generative model for industrial anomaly detection via bi-directional LSTM and attention mechanism. IEEE Trans. Ind. Inform. (2021). https:\/\/doi.org\/10.1109\/TII.2021.3078192","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"8","key":"3719_CR13","doi-asserted-by":"publisher","first-page":"5244","DOI":"10.1109\/TII.2019.2952917","volume":"16","author":"D Wu","year":"2019","unstructured":"Wu, D., Jiang, Z., Xie, X., Wei, X., Yu, W., Li, R.: LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT. IEEE Trans. Ind. Inform. 16(8), 5244\u20135253 (2019)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"10","key":"3719_CR14","doi-asserted-by":"publisher","first-page":"7110","DOI":"10.1109\/JIOT.2021.3074382","volume":"9","author":"X Wang","year":"2021","unstructured":"Wang, X., Garg, S., Lin, H., Hu, J., Kaddoum, G., Piran, M.J., Hossain, M.S.: Towards accurate anomaly detection in industrial internet-of-things using hierarchical federated learning. IEEE Internet Things J. 9(10), 7110\u20137119 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"12","key":"3719_CR15","doi-asserted-by":"publisher","first-page":"9214","DOI":"10.1109\/JIOT.2021.3094295","volume":"9","author":"Y Wu","year":"2021","unstructured":"Wu, Y., Dai, H.N., Tang, H.: Graph neural networks for anomaly detection in industrial internet of things. IEEE Internet Things J. 9(12), 9214\u20139231 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"5","key":"3719_CR16","doi-asserted-by":"publisher","first-page":"3469","DOI":"10.1109\/TII.2020.3022432","volume":"17","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Hu, Y., Liang, W., Ma, J., Jin, Q.: Variational LSTM enhanced anomaly detection for industrial big data. IEEE Trans. Ind. Inform. 17(5), 3469\u20133477 (2020)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"3719_CR17","doi-asserted-by":"crossref","unstructured":"De Vita, F., Bruneo, D., Das, S.K.: A novel data collection framework for telemetry and anomaly detection in industrial iot systems. In: 2020 IEEE\/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI, pp. 245\u2013251. IEEE (2020)","DOI":"10.1109\/IoTDI49375.2020.00032"},{"issue":"8","key":"3719_CR18","doi-asserted-by":"publisher","first-page":"10725","DOI":"10.1007\/s11227-021-04284-4","volume":"78","author":"ST Ikram","year":"2022","unstructured":"Ikram, S.T., Priya, V., Anbarasu, B., Cheng, X., Ghalib, M.R., Shankar, A.: Prediction of IIoT traffic using a modified whale optimization approach integrated with random forest classifier. J. Supercomput. 78(8), 10725\u201310756 (2022)","journal-title":"J. Supercomput."},{"issue":"9","key":"3719_CR19","doi-asserted-by":"publisher","first-page":"6182","DOI":"10.1109\/TII.2020.2975227","volume":"16","author":"X Yan","year":"2020","unstructured":"Yan, X., Xu, Y., Xing, X., Cui, B., Guo, Z., Guo, T.: Trustworthy network anomaly detection based on an adaptive learning rate and momentum in IIoT. IEEE Trans. Ind. Inform. 16(9), 6182\u20136192 (2020)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"3719_CR20","doi-asserted-by":"publisher","first-page":"59406","DOI":"10.1109\/ACCESS.2021.3072916","volume":"9","author":"M Savic","year":"2021","unstructured":"Savic, M., Lukic, M., Danilovic, D., Bodroski, Z., Bajovi\u0107, D., Mezei, I., Vukobratovic, D., Skrbic, S., Jakoveti\u0107, D.: Deep learning anomaly detection for cellular IoT with applications in smart logistics. IEEE Access 9, 59406\u201359419 (2021)","journal-title":"IEEE Access"},{"key":"3719_CR21","doi-asserted-by":"crossref","unstructured":"Jose, A., Jeba, S.V.A., Jose, B.J.: A novel missing data imputation algorithm for deep learning-based anomaly detection system in IIoT networks. In: Smart Computing and Self-Adaptive Systems, pp. 27\u201346. CRC Press (2021)","DOI":"10.1201\/9781003156123-2"},{"issue":"9","key":"3719_CR22","doi-asserted-by":"publisher","first-page":"6406","DOI":"10.1109\/TII.2022.3149902","volume":"18","author":"A Makkar","year":"2022","unstructured":"Makkar, A., Kim, T.W., Singh, A.K., Kang, J., Park, J.H.: SecureIIoT environment: federated learning empowered approach for securing IIoT from data breach. IEEE Trans. Ind. Inform. 18(9), 6406\u20136414 (2022)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"3719_CR23","first-page":"1","volume":"2022","author":"F Shi","year":"2022","unstructured":"Shi, F., Yan, L., Zhao, X., Xian-Ke, R.: Machine learning-based time-series data analysis in edge-cloud-assisted oil industrial IoT system. Mob. Inf. Syst. 2022, 1\u201311 (2022)","journal-title":"Mob. Inf. Syst."},{"key":"3719_CR24","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1007\/s10586-022-03549-8","volume":"25","author":"S Balakrishna","year":"2022","unstructured":"Balakrishna, S.: Multi objective-based incremental clustering by fast search technique for dynamically creating and updating clusters in large data. Cluster Comput. 25, 1441\u20131457 (2022). https:\/\/doi.org\/10.1007\/s10586-022-03549-8","journal-title":"Cluster Comput."},{"key":"3719_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-022-04427-1","author":"S Balakrishna","year":"2022","unstructured":"Balakrishna, S.: D-ACSM: a technique for dynamically assigning and adjusting cluster patterns for IoT data analysis. J. Supercomput. (2022). https:\/\/doi.org\/10.1007\/s11227-022-04427-1","journal-title":"J. Supercomput."},{"key":"3719_CR26","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxy133","author":"G Brammya","year":"2019","unstructured":"Brammya, G., Praveena, S., Ninu Preetha, N.S., Ramya, R., Rajakumar, B.R., Binu, D.: Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comput. J. (2019). https:\/\/doi.org\/10.1093\/comjnl\/bxy133","journal-title":"Comput. J."},{"key":"3719_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107878","volume":"113","author":"K Shankar","year":"2021","unstructured":"Shankar, K., Perumal, E., D\u00edaz, V.G., Tiwari, P., Gupta, D., Saudagar, A.K.J., Muhammad, K.: An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images. Appl. Soft Comput. 113, 107878 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"3719_CR28","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","volume":"8","author":"J Xue","year":"2020","unstructured":"Xue, J., Shen, B.: A novel swarm intelligence optimization approach: sparrow search algorithm. Syst. Sci. Control Eng. 8(1), 22\u201334 (2020)","journal-title":"Syst. Sci. Control Eng."},{"key":"3719_CR29","unstructured":"https:\/\/www.kaggle.com\/mrwellsdavid\/unsw-nb15"},{"key":"3719_CR30","unstructured":"https:\/\/www.kaggle.com\/paresh2047\/uci-semcom"},{"issue":"1","key":"3719_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00379-6","volume":"7","author":"SM Kasongo","year":"2020","unstructured":"Kasongo, S.M., Sun, Y.: Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset. J. Big Data 7(1), 1\u201320 (2020)","journal-title":"J. Big Data"},{"key":"3719_CR32","doi-asserted-by":"publisher","first-page":"7835","DOI":"10.3390\/s21237835","volume":"21","author":"K Kotecha","year":"2021","unstructured":"Kotecha, K., Verma, R., Rao, P.V., Prasad, P., Mishra, V.K., Badal, T., Jain, D., Garg, D., Sharma, S.: Enhanced network intrusion detection system. Sensors 21, 7835 (2021). https:\/\/doi.org\/10.3390\/s21237835","journal-title":"Sensors"},{"key":"3719_CR33","doi-asserted-by":"crossref","unstructured":"Moldovan, D., Anghel, I., Cioara, T., Salomie, I.: Particle swarm optimization based deep learning ensemble for manufacturing processes. In: 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 563\u2013570. IEEE (2020)","DOI":"10.1109\/ICCP51029.2020.9266269"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03719-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-022-03719-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03719-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T19:08:52Z","timestamp":1684868932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-022-03719-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["3719"],"URL":"https:\/\/doi.org\/10.1007\/s10586-022-03719-8","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,25]]},"assertion":[{"value":"6 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}