{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T22:22:49Z","timestamp":1781130169118,"version":"3.54.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"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":[[2025,10]]},"DOI":"10.1007\/s10586-025-05404-y","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T14:19:15Z","timestamp":1756909155000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing intrusion detection: protocol-based security using a hybrid RIDGE classifier on InSDN, UNSW-NB15, BoT-IoT, and ToN-IoT datasets"],"prefix":"10.1007","volume":"28","author":[{"given":"Anand","family":"Nemalikanti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sreenija","family":"Kaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rami Reddy","family":"Ambati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raveendra Babu","family":"Ponnuru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"issue":"8","key":"5404_CR1","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1109\/TCYB.2015.2490802","volume":"46","author":"OY Al-Jarrah","year":"2015","unstructured":"Al-Jarrah, O.Y., Alhussein, O., Yoo, P.D., Muhaidat, S., Taha, K., Kim, K.: Data randomization and cluster-based partitioning for botnet intrusion detection. IEEE Trans. Cybern. 46(8), 1796\u20131806 (2015)","journal-title":"IEEE Trans. Cybern."},{"issue":"12","key":"5404_CR2","doi-asserted-by":"publisher","first-page":"9463","DOI":"10.1109\/JIOT.2020.2996590","volume":"8","author":"O Alkadi","year":"2020","unstructured":"Alkadi, O., Moustafa, N., Turnbull, B., Choo, K.-K.R.: A deep blockchain framework-enabled collaborative intrusion detection for protecting iot and cloud networks. IEEE Internet Things J. 8(12), 9463\u20139472 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"18","key":"5404_CR3","doi-asserted-by":"publisher","first-page":"8383","DOI":"10.3390\/app11188383","volume":"11","author":"MA Alsoufi","year":"2021","unstructured":"Alsoufi, M.A., Razak, S., Siraj, M.M., Nafea, I., Ghaleb, F.A., Saeed, F., Nasser, M.: Anomaly-based intrusion detection systems in iot using deep learning: a systematic literature review. Appl. Sci. 11(18), 8383 (2021)","journal-title":"Appl. Sci."},{"key":"5404_CR4","doi-asserted-by":"publisher","first-page":"28645","DOI":"10.1109\/ACCESS.2023.3255646","volume":"11","author":"N Anand","year":"2023","unstructured":"Anand, N., Saifulla, M.A.: En-lakp: lightweight authentication and key agreement protocol for emerging networks. IEEE Access 11, 28645\u201328657 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3255646","journal-title":"IEEE Access"},{"issue":"11","key":"5404_CR5","doi-asserted-by":"publisher","first-page":"11338","DOI":"10.1109\/TVT.2021.3116279","volume":"70","author":"PR Babu","year":"2021","unstructured":"Babu, P.R., Amin, R., Reddy, A.G., Das, A.K., Susilo, W., Park, Y.: Robust authentication protocol for dynamic charging system of electric vehicles. IEEE Trans. Veh. Technol. 70(11), 11338\u201311351 (2021)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"3","key":"5404_CR6","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1002\/spy2.210","volume":"5","author":"PR Babu","year":"2022","unstructured":"Babu, P.R., Palaniswamy, B., Reddy, A.G., Odelu, V., Kim, H.S.: A survey on security challenges and protocols of electric vehicle dynamic charging system. Secur. Privacy 5(3), 210 (2022)","journal-title":"Secur. Privacy"},{"issue":"3","key":"5404_CR7","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TIV.2022.3153658","volume":"7","author":"PR Babu","year":"2022","unstructured":"Babu, P.R., Reddy, A.G., Palaniswamy, B., Kommuri, S.K.: Ev-auth: lightweight authentication protocol suite for dynamic charging system of electric vehicles with seamless handover. IEEE Trans. Intell. Veh. 7(3), 734\u2013747 (2022)","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"5","key":"5404_CR8","doi-asserted-by":"publisher","first-page":"3791","DOI":"10.1109\/TNSE.2022.3186949","volume":"9","author":"PR Babu","year":"2022","unstructured":"Babu, P.R., Reddy, A.G., Palaniswamy, B., Das, A.K.: Ev-puf: lightweight security protocol for dynamic charging system of electric vehicles using physical unclonable functions. IEEE Trans. Netw. Sci. Eng. 9(5), 3791\u20133807 (2022)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"6","key":"5404_CR9","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.1109\/JIOT.2017.2746186","volume":"4","author":"S Bera","year":"2017","unstructured":"Bera, S., Misra, S., Vasilakos, A.V.: Software-defined networking for internet of things: a survey. IEEE Internet Things J. 4(6), 1994\u20132008 (2017)","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"5404_CR10","doi-asserted-by":"publisher","first-page":"446","DOI":"10.3390\/s21020446","volume":"21","author":"A Churcher","year":"2021","unstructured":"Churcher, A., Ullah, R., Ahmad, J., Ur Rehman, S., Masood, F., Gogate, M., Alqahtani, F., Nour, B., Buchanan, W.J.: An experimental analysis of attack classification using machine learning in iot networks. Sensors 21(2), 446 (2021)","journal-title":"Sensors"},{"key":"5404_CR11","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.ins.2013.03.022","volume":"239","author":"I Corona","year":"2013","unstructured":"Corona, I., Giacinto, G., Roli, F.: Adversarial attacks against intrusion detection systems: taxonomy, solutions and open issues. Inf. Sci. 239, 201\u2013225 (2013)","journal-title":"Inf. Sci."},{"key":"5404_CR12","doi-asserted-by":"publisher","first-page":"165263","DOI":"10.1109\/ACCESS.2020.3022633","volume":"8","author":"MS Elsayed","year":"2020","unstructured":"Elsayed, M.S., Le-Khac, N.-A., Jurcut, A.D.: Insdn: A novel sdn intrusion dataset. IEEE Access 8, 165263\u2013165284 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3022633","journal-title":"IEEE Access"},{"issue":"1","key":"5404_CR13","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1038\/s41598-021-04419-w","volume":"12","author":"Y Essam","year":"2022","unstructured":"Essam, Y., Huang, Y.F., Birima, A.H., Ahmed, A.N., El-Shafie, A.: Predicting suspended sediment load in peninsular malaysia using support vector machine and deep learning algorithms. Sci. Rep. 12(1), 302 (2022)","journal-title":"Sci. Rep."},{"issue":"6","key":"5404_CR14","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MCOM.2015.7120042","volume":"53","author":"F Granelli","year":"2015","unstructured":"Granelli, F., Gebremariam, A.A., Usman, M., Cugini, F., Stamati, V., Alitska, M., Chatzimisios, P.: Software defined and virtualized wireless access in future wireless networks: scenarios and standards. IEEE Commun. Mag. 53(6), 26\u201334 (2015)","journal-title":"IEEE Commun. Mag."},{"issue":"7","key":"5404_CR15","doi-asserted-by":"publisher","first-page":"10612","DOI":"10.1016\/j.eswa.2009.02.054","volume":"36","author":"E Hern\u00e1ndez-Pereira","year":"2009","unstructured":"Hern\u00e1ndez-Pereira, E., Su\u00e1rez-Romero, J.A., Fontenla-Romero, O., Alonso-Betanzos, A.: Conversion methods for symbolic features: A comparison applied to an intrusion detection problem. Expert Syst. Appl. 36(7), 10612\u201310617 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"5404_CR16","doi-asserted-by":"publisher","first-page":"0155781","DOI":"10.1371\/journal.pone.0155781","volume":"11","author":"M-J Kang","year":"2016","unstructured":"Kang, M.-J., Kang, J.-W.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS One 11(6), 0155781 (2016)","journal-title":"PLoS One"},{"key":"5404_CR17","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\u201320 (2020)","journal-title":"J. Big Data"},{"issue":"21","key":"5404_CR18","doi-asserted-by":"publisher","first-page":"7016","DOI":"10.3390\/s21217016","volume":"21","author":"MA Khan","year":"2021","unstructured":"Khan, M.A., Khan, M.A., Jan, S.U., Ahmad, J., Jamal, S.S., Shah, A.A., Pitropakis, N., Buchanan, W.J.: A deep learning-based intrusion detection system for mqtt enabled iot. Sensors 21(21), 7016 (2021)","journal-title":"Sensors"},{"issue":"11","key":"5404_CR19","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.3390\/electronics8111210","volume":"8","author":"A Khraisat","year":"2019","unstructured":"Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., Alazab, A.: A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electronics 8(11), 1210 (2019)","journal-title":"Electronics"},{"key":"5404_CR20","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Fut. Gen. Comput. Syst. 100, 779\u2013796 (2019)","journal-title":"Fut. Gen. Comput. Syst."},{"issue":"2","key":"5404_CR21","doi-asserted-by":"publisher","first-page":"656","DOI":"10.3390\/s21020656","volume":"21","author":"X Larriva-Novo","year":"2021","unstructured":"Larriva-Novo, X., Villagr\u00e1, V.A., Vega-Barbas, M., Rivera, D., Sanz Rodrigo, M.: An iot-focused intrusion detection system approach based on preprocessing characterization for cybersecurity datasets. Sensors 21(2), 656 (2021)","journal-title":"Sensors"},{"key":"5404_CR22","doi-asserted-by":"publisher","first-page":"22351","DOI":"10.1109\/ACCESS.2021.3056614","volume":"9","author":"ZK Maseer","year":"2021","unstructured":"Maseer, Z.K., Yusof, R., Bahaman, N., Mostafa, S.A., Foozy, C.F.M.: Benchmarking of machine learning for anomaly based intrusion detection systems in the cicids2017 dataset. IEEE Access 9, 22351\u201322370 (2021)","journal-title":"IEEE Access"},{"key":"5404_CR23","doi-asserted-by":"publisher","first-page":"3609","DOI":"10.1007\/s12652-019-01611-9","volume":"12","author":"M Mayuranathan","year":"2021","unstructured":"Mayuranathan, M., Murugan, M., Dhanakoti, V.: Best features based intrusion detection system by rbm model for detecting ddos in cloud environment. J. Ambient. Intell. Humaniz. Comput. 12, 3609\u20133619 (2021)","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"issue":"5","key":"5404_CR24","first-page":"478","volume":"8","author":"S Meftah","year":"2019","unstructured":"Meftah, S., Rachidi, T., Assem, N.: Network based intrusion detection using the unsw-nb15 dataset. Int. J. Comput. Digit. Syst. 8(5), 478\u2013487 (2019)","journal-title":"Int. J. Comput. Digit. Syst."},{"issue":"14","key":"5404_CR25","doi-asserted-by":"publisher","first-page":"4736","DOI":"10.3390\/s21144736","volume":"21","author":"ST Mehedi","year":"2021","unstructured":"Mehedi, S.T., Anwar, A., Rahman, Z., Ahmed, K.: Deep transfer learning based intrusion detection system for electric vehicular networks. Sensors 21(14), 4736 (2021)","journal-title":"Sensors"},{"key":"5404_CR26","first-page":"91","volume":"70","author":"M Mehmood","year":"2022","unstructured":"Mehmood, M., Javed, T., Nebhen, J., Abbas, S., Abid, R., Bojja, G.R., Rizwan, M.: A hybrid approach for network intrusion detection. CMC-Comput. Mater. Contin 70, 91\u2013107 (2022)","journal-title":"CMC-Comput. Mater. Contin"},{"issue":"1","key":"5404_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2808691","volume":"48","author":"A Milenkoski","year":"2015","unstructured":"Milenkoski, A., Vieira, M., Kounev, S., Avritzer, A., Payne, B.D.: Evaluating computer intrusion detection systems: a survey of common practices. ACM Comput. Surv. (CSUR) 48(1), 1\u201341 (2015)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"3A","key":"5404_CR28","first-page":"478","volume":"19","author":"IK Nti","year":"2022","unstructured":"Nti, I.K., Narko-Boateng, O., Adekoya, A.F., Somanathan, A.R.: Stacknet based decision fusion classifier for network intrusion detection. Int. Arab J. Inform. Technol. 19(3A), 478\u2013490 (2022)","journal-title":"Int. Arab J. Inform. Technol."},{"issue":"9","key":"5404_CR29","doi-asserted-by":"publisher","first-page":"2985","DOI":"10.3390\/s21092985","volume":"21","author":"SI Popoola","year":"2021","unstructured":"Popoola, S.I., Adebisi, B., Ande, R., Hammoudeh, M., Anoh, K., Atayero, A.A.: smote-drnn: a deep learning algorithm for botnet detection in the internet-of-things networks. Sensors 21(9), 2985 (2021)","journal-title":"Sensors"},{"key":"5404_CR30","doi-asserted-by":"publisher","first-page":"3343","DOI":"10.1109\/ACCESS.2019.2962829","volume":"8","author":"S Pundir","year":"2019","unstructured":"Pundir, S., Wazid, M., Singh, D.P., Das, A.K., Rodrigues, J.J., Park, Y.: Intrusion detection protocols in wireless sensor networks integrated to internet of things deployment: survey and future challenges. IEEE Access 8, 3343\u20133363 (2019)","journal-title":"IEEE Access"},{"key":"5404_CR31","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.comnet.2018.11.010","volume":"148","author":"F Salo","year":"2019","unstructured":"Salo, F., Nassif, A.B., Essex, A.: Dimensionality reduction with ig-pca and ensemble classifier for network intrusion detection. Comput. Netw. 148, 164\u2013175 (2019)","journal-title":"Comput. Netw."},{"issue":"5","key":"5404_CR32","doi-asserted-by":"publisher","first-page":"3242","DOI":"10.1109\/JIOT.2020.3002255","volume":"8","author":"M Shafiq","year":"2020","unstructured":"Shafiq, M., Tian, Z., Bashir, A.K., Du, X., Guizani, M.: Corrauc: a malicious bot-iot traffic detection method in iot network using machine-learning techniques. IEEE Internet Things J. 8(5), 3242\u20133254 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"5404_CR33","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1007\/s11277-021-08917-y","volume":"122","author":"DS Vijayakumar","year":"2022","unstructured":"Vijayakumar, D.S., Ganapathy, S.: Multistage ensembled classifier for wireless intrusion detection system. Wireless Pers. Commun. 122(1), 645\u2013668 (2022)","journal-title":"Wireless Pers. Commun."},{"issue":"1","key":"5404_CR34","first-page":"1","volume":"1","author":"P Wanda","year":"2020","unstructured":"Wanda, P., Jie, H.J.: A survey of intrusion detection system. Int. J. Inform. Comput. 1(1), 1\u201310 (2020)","journal-title":"Int. J. Inform. Comput."},{"issue":"11","key":"5404_CR35","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.3390\/s19112528","volume":"19","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Zheng, K., Wu, C., Yang, Y.: Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors 19(11), 2528 (2019)","journal-title":"Sensors"},{"key":"5404_CR36","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1109\/ACCESS.2021.3137201","volume":"10","author":"M Zeeshan","year":"2021","unstructured":"Zeeshan, M., Riaz, Q., Bilal, M.A., Shahzad, M.K., Jabeen, H., Haider, S.A., Rahim, A.: Protocol-based deep intrusion detection for dos and ddos attacks using unsw-nb15 and bot-iot data-sets. IEEE Access 10, 2269\u20132283 (2021)","journal-title":"IEEE Access"},{"issue":"1","key":"5404_CR37","first-page":"68","volume":"53","author":"L Zhang","year":"2016","unstructured":"Zhang, L., Zhang, Y.: Infinite depth neural network method for big data analysis. Comput. Res. Dev. 53(1), 68\u201379 (2016)","journal-title":"Comput. Res. Dev."},{"key":"5404_CR38","doi-asserted-by":"crossref","unstructured":"Ma, T., Yu, Y., Wang, F., Zhang, Q., Chen, X.: A hybrid methodologies for intrusion detection based deep neural network with support vector machine and clustering technique. In: Frontier Computing: Theory, Technologies and Applications FC 2016 5, pp. 123\u2013134. Springer. (2018)","DOI":"10.1007\/978-981-10-3187-8_13"},{"key":"5404_CR39","doi-asserted-by":"crossref","unstructured":"Tian, C., Zhang, F., Li, Z., Wang, R., Huang, X., Xi, L., Zhang, Y.: Intrusion detection method based on deep learning. Wirel. Commun. Mob. Comput. 2022 (2022)","DOI":"10.1155\/2022\/1338392"},{"key":"5404_CR40","doi-asserted-by":"crossref","unstructured":"Longari, S., Penco, M., Carminati, M., Zanero, S.: Copycan: an error-handling protocol based intrusion detection system for controller area network. In: Proceedings of the ACM Workshop on Cyber-Physical Systems Security & Privacy, pp. 39\u201350 (2019)","DOI":"10.1145\/3338499.3357362"},{"key":"5404_CR41","doi-asserted-by":"crossref","unstructured":"Anand, N., MA, S., Aakula, P.K., Ponnuru, R.B., Patan, R., Reddy, C.R.P.: Enhancing intrusion detection against denial of service and distributed denial of service attacks: Leveraging extended berkeley packet filter and machine learning algorithms. IET Commun. 19(1), 12879 (2025)","DOI":"10.1049\/cmu2.12879"},{"key":"5404_CR42","doi-asserted-by":"crossref","unstructured":"Anand, N., Saifulla, M., Ponnuru, R.B., Alavalapati, G.R., Patan, R., Gandomi, A.H.: Securing software defined networks: a comprehensive analysis of approaches, applications, and future strategies against dos attacks. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3520478"},{"key":"5404_CR43","doi-asserted-by":"crossref","unstructured":"Anand, N., Saifulla, M.: A probabilistic method to identify http\/1.1 slow rate dos attacks. In: International Conference on Communication and Intelligent Systems, pp. 17\u201328. Springer (2022)","DOI":"10.1007\/978-981-99-2322-9_2"},{"key":"5404_CR44","doi-asserted-by":"crossref","unstructured":"Anand, N., Saifulla, M., Raja Ashok&nbsp;Reddy, G., Pavan, P.: Effective encrypted traffic analysis. In: International Conference on Advanced Computing and Intelligent Engineering, pp. 395\u2013400. Springer (2022)","DOI":"10.1007\/978-981-99-5015-7_33"},{"key":"5404_CR45","doi-asserted-by":"crossref","unstructured":"Pavan, P., Saifulla, M., Anand, N.: Improvising encrypted traffic analysis using stacking ensemble model. In: International Conference on Advanced Computing and Intelligent Engineering, pp. 387\u2013394. Springer (2022)","DOI":"10.1007\/978-981-99-5015-7_32"},{"key":"5404_CR46","doi-asserted-by":"crossref","unstructured":"Anand, N., Saifulla, M.: An efficient ids for slow rate http\/2.0 dos attacks using one class classification. In: 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), pp. 1\u20139 . IEEE(2023)","DOI":"10.1109\/I2CT57861.2023.10126162"},{"key":"5404_CR47","doi-asserted-by":"crossref","unstructured":"Anthi, E., Williams, L., Burnap, P.: Pulse: an adaptive intrusion detection for the internet of things (2018)","DOI":"10.1049\/cp.2018.0035"},{"key":"5404_CR48","unstructured":"Weka 3: Machine learning software in Java. Available: https:\/\/www.cs.waikato.ac.nz\/ml\/weka\/"},{"key":"5404_CR49","doi-asserted-by":"crossref","unstructured":"Nobakht, M., Sivaraman, V., Boreli, R.: A host-based intrusion detection and mitigation framework for smart home iot using openflow. In: 2016 11th International Conference on Availability, Reliability and Security (ARES), pp. 147\u2013156. IEEE (2016)","DOI":"10.1109\/ARES.2016.64"},{"key":"5404_CR50","doi-asserted-by":"crossref","unstructured":"Ibitoye, O., Shafiq, O., Matrawy, A.: Analyzing adversarial attacks against deep learning for intrusion detection in iot networks. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/GLOBECOM38437.2019.9014337"},{"key":"5404_CR51","doi-asserted-by":"crossref","unstructured":"Leevy, J.L., Khoshgoftaar, T.M., Peterson, J.M.: Mitigating class imbalance for iot network intrusion detection: a survey. In: 2021 IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService), pp. 143\u2013148. IEEE (2021)","DOI":"10.1109\/BigDataService52369.2021.00023"},{"key":"5404_CR52","doi-asserted-by":"publisher","unstructured":"Moustafa, N.: Ton_iot datasets (2019). https:\/\/doi.org\/10.21227\/fesz-dm97","DOI":"10.21227\/fesz-dm97"},{"key":"5404_CR53","doi-asserted-by":"crossref","unstructured":"Woo, J.-h., Song, J.-Y., Choi, Y.-J.: Performance enhancement of deep neural network using feature selection and preprocessing for intrusion detection. In: 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 415\u2013417. IEEE (2019)","DOI":"10.1109\/ICAIIC.2019.8668995"},{"key":"5404_CR54","doi-asserted-by":"crossref","unstructured":"Laskov, P., D\u00fcssel, P., Sch\u00e4fer, C., Rieck, K.: Learning intrusion detection: supervised or unsupervised? In: Image Analysis and Processing\u2013ICIAP 2005: 13th International Conference, Cagliari, Italy, September 6-8, 2005. Proceedings 13, pp. 50\u201357. Springer (2005)","DOI":"10.1007\/11553595_6"},{"key":"5404_CR55","doi-asserted-by":"crossref","unstructured":"Yeung, D.-Y., Ding, Y.: User profiling for intrusion detection using dynamic and static behavioral models. In: Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002 Taipei, Taiwan, May 6\u20138, 2002 Proceedings, pp. 494\u2013505 (2002). Springer","DOI":"10.1007\/3-540-47887-6_49"},{"key":"5404_CR56","doi-asserted-by":"crossref","unstructured":"Farahnakian, F., Heikkonen, J.: A deep auto-encoder based approach for intrusion detection system. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp. 178\u2013183 (2018). IEEE","DOI":"10.23919\/ICACT.2018.8323688"},{"key":"5404_CR57","doi-asserted-by":"crossref","unstructured":"Kunang, Y.N., Nurmaini, S., Stiawan, D., Zarkasi, A., et al.: Automatic features extraction using autoencoder in intrusion detection system. In: 2018 International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 219\u2013224. IEEE (2018)","DOI":"10.1109\/ICECOS.2018.8605181"},{"key":"5404_CR58","doi-asserted-by":"crossref","unstructured":"Ao, H.: Using machine learning models to detect different intrusion on nsl-kdd. In: 2021 IEEE International Conference on Computer Science. In: Artificial Intelligence and Electronic Engineering (CSAIEE), pp. 166\u2013177 (2021). IEEE","DOI":"10.1109\/CSAIEE54046.2021.9543241"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05404-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05404-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05404-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T09:36:29Z","timestamp":1759743389000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05404-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"references-count":58,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["5404"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05404-y","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,3]]},"assertion":[{"value":"16 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"663"}}