{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:49:22Z","timestamp":1782316162810,"version":"3.54.5"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"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":[[2025,4]]},"DOI":"10.1007\/s10586-024-04744-5","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T18:50:21Z","timestamp":1732647021000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["AE-CIAM: a hybrid AI-enabled framework for low-rate DDoS attack detection in cloud computing"],"prefix":"10.1007","volume":"28","author":[{"given":"Ashfaq Ahmad","family":"Najar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Manohar Naik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"key":"4744_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122544","author":"O Pandithurai","year":"2023","unstructured":"Pandithurai, O., Venkataiah, C., Tiwari, S., Ramanjaneyulu, N.: DDoS attack prediction using a honey badger optimization algorithm based feature selection and Bi-LSTM in cloud environment. Expert Syst. Appl. (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.122544","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"4744_CR2","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1109\/TNSM.2021.3097903","volume":"19","author":"IR Divyasree","year":"2022","unstructured":"Divyasree, I.R., Selvamani, K.: Dad: domain adversarial defense system against DDoS attacks in cloud. IEEE Trans. Netw. Serv. Manag. 19(1), 554\u2013568 (2022). https:\/\/doi.org\/10.1109\/TNSM.2021.3097903","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"4744_CR3","doi-asserted-by":"publisher","first-page":"43920","DOI":"10.1109\/ACCESS.2020.2976609","volume":"8","author":"W Zhijun","year":"2020","unstructured":"Zhijun, W., Wenjing, L., Liang, L., Meng, Y.: Low-rate dos attacks, detection, defense, and challenges: a survey. IEEE Access 8, 43920\u201343943 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2976609","journal-title":"IEEE Access"},{"key":"4744_CR4","doi-asserted-by":"publisher","first-page":"102532","DOI":"10.1016\/j.jisa.2020.102532","volume":"53","author":"GS Kushwah","year":"2020","unstructured":"Kushwah, G.S., Ranga, V.: Voting extreme learning machine based distributed denial of service attack detection in cloud computing. J. Inf. Secur. Appl. 53, 102532 (2020). https:\/\/doi.org\/10.1016\/j.jisa.2020.102532","journal-title":"J. Inf. Secur. Appl."},{"key":"4744_CR5","doi-asserted-by":"publisher","first-page":"53972","DOI":"10.1109\/ACCESS.2020.2976908","volume":"8","author":"S Haider","year":"2020","unstructured":"Haider, S., Akhunzada, A., Mustafa, I., Patel, T.B., Fernandez, A., Choo, K.-K.R., Iqbal, J.: A deep CNN ensemble framework for efficient DDoS attack detection in software defined networks. IEEE Access 8, 53972\u201353983 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2976908","journal-title":"IEEE Access"},{"issue":"4","key":"4744_CR6","doi-asserted-by":"publisher","first-page":"2813","DOI":"10.1007\/s13369-019-04178-x","volume":"45","author":"P Verma","year":"2020","unstructured":"Verma, P., Tapaswi, S., Godfrey, W.W.: An adaptive threshold-based attribute selection to classify requests under DDoS attack in cloud-based systems. Arab. J. Sci. Eng. 45(4), 2813\u20132834 (2020). https:\/\/doi.org\/10.1007\/s13369-019-04178-x","journal-title":"Arab. J. Sci. Eng."},{"key":"4744_CR7","doi-asserted-by":"publisher","first-page":"102260","DOI":"10.1016\/j.jisa.2020.102532","volume":"105","author":"GS Kushwah","year":"2021","unstructured":"Kushwah, G.S., Ranga, V.: Optimized extreme learning machine for detecting DDoS attacks in cloud computing. Comput. Secur. 105, 102260 (2021). https:\/\/doi.org\/10.1016\/j.jisa.2020.102532","journal-title":"Comput. Secur."},{"key":"4744_CR8","doi-asserted-by":"publisher","first-page":"102725","DOI":"10.1016\/j.cose.2022.102725","volume":"118","author":"H Ayd\u0131n","year":"2022","unstructured":"Ayd\u0131n, H., Orman, Z., Ayd\u0131n, M.A.: A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment. Comput. Secur. 118, 102725 (2022). https:\/\/doi.org\/10.1016\/j.cose.2022.102725","journal-title":"Comput. Secur."},{"key":"4744_CR9","doi-asserted-by":"publisher","first-page":"109138","DOI":"10.1016\/j.comnet.2022.109138","volume":"215","author":"GA MM","year":"2022","unstructured":"MM, G.A., S, J.N.K., R, U.M., TF, M.R.: An efficient SVM based DEHO classifier to detect DDoS attack in cloud computing environment. Comput. Netw. 215, 109138 (2022). https:\/\/doi.org\/10.1016\/j.comnet.2022.109138","journal-title":"Comput. Netw."},{"key":"4744_CR10","doi-asserted-by":"publisher","first-page":"103445","DOI":"10.1016\/j.jisa.2023.103445","volume":"74","author":"US Chanu","year":"2023","unstructured":"Chanu, U.S., Singh, K.J., Chanu, Y.J.: A dynamic feature selection technique to detect DDoS attack. J. Inf. Secur. Appl. 74, 103445 (2023). https:\/\/doi.org\/10.1016\/j.jisa.2023.103445","journal-title":"J. Inf. Secur. Appl."},{"key":"4744_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-023-02225-4","author":"P Verma","year":"2023","unstructured":"Verma, P., Kowsik, A.R.K., Pateriya, R.K., Bharot, N., Vidyarthi, A., Gupta, D.: A stacked ensemble approach to generalize the classifier prediction for the detection of DDoS attack in cloud network. Mob. Netw. Appl. (2023). https:\/\/doi.org\/10.1007\/s11036-023-02225-4","journal-title":"Mob. Netw. Appl."},{"key":"4744_CR12","doi-asserted-by":"publisher","first-page":"100828","DOI":"10.1016\/j.measen.2023.100828","volume":"28","author":"M Pasha","year":"2023","unstructured":"Pasha, M., Rao, K., MallaReddy, A., Bande, V.: LRDADF: an AI enabled framework for detecting low-rate DDoS attacks in cloud computing environments. Meas. Sens. 28, 100828 (2023). https:\/\/doi.org\/10.1016\/j.measen.2023.100828","journal-title":"Meas. Sens."},{"key":"4744_CR13","doi-asserted-by":"publisher","first-page":"102107","DOI":"10.1016\/j.cose.2020.102107","volume":"100","author":"X Liu","year":"2021","unstructured":"Liu, X., Ren, J., He, H., Wang, Q., Song, C.: Low-rate DDoS attacks detection method using data compression and behavior divergence measurement. Comput. Secur. 100, 102107 (2021). https:\/\/doi.org\/10.1016\/j.cose.2020.102107","journal-title":"Comput. Secur."},{"key":"4744_CR14","doi-asserted-by":"crossref","first-page":"108498","DOI":"10.1016\/j.comnet.2021.108498","volume":"200","author":"RK Batchu","year":"2021","unstructured":"Batchu, R.K., Seetha, H.: A generalized machine learning model for DDoS attacks detection using hybrid feature selection and hyperparameter tuning. Comput. Netw. 200, 108498 (2021)","journal-title":"Comput. Netw."},{"key":"4744_CR15","doi-asserted-by":"crossref","first-page":"102748","DOI":"10.1016\/j.cose.2022.102748","volume":"118","author":"D Akgun","year":"2022","unstructured":"Akgun, D., Hizal, S., Cavusoglu, U.: A new DDoS attacks intrusion detection model based on deep learning for cybersecurity. Comput. Secur. 118, 102748 (2022)","journal-title":"Comput. Secur."},{"issue":"7","key":"4744_CR16","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1093\/comjnl\/bxz064","volume":"63","author":"M Asad","year":"2020","unstructured":"Asad, M., Asim, M., Javed, T., Beg, M.O., Mujtaba, H., Abbas, S.: DeepDetect: detection of distributed denial of service attacks using deep learning. Comput. J. 63(7), 983\u2013994 (2020)","journal-title":"Comput. J."},{"key":"4744_CR17","doi-asserted-by":"publisher","first-page":"114520","DOI":"10.1016\/j.eswa.2020.114520","volume":"169","author":"AE Cil","year":"2021","unstructured":"Cil, A.E., Yildiz, K., Buldu, A.: Detection of DDoS attacks with feed forward based deep neural network model. Expert Syst. Appl. 169, 114520 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2020.114520","journal-title":"Expert Syst. Appl."},{"key":"4744_CR18","doi-asserted-by":"publisher","first-page":"155859","DOI":"10.1109\/ACCESS.2020.3019330","volume":"8","author":"JA P\u00e9rez-D\u00edaz","year":"2020","unstructured":"P\u00e9rez-D\u00edaz, J.A., Valdovinos, I.A., Choo, K.-K.R., Zhu, D.: A flexible SDN-based architecture for identifying and mitigating low-rate DDoS attacks using machine learning. IEEE Access 8, 155859\u2013155872 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3019330","journal-title":"IEEE Access"},{"key":"4744_CR19","doi-asserted-by":"publisher","first-page":"42120","DOI":"10.1109\/ACCESS.2020.2976706","volume":"8","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Yin, X., Hu, Y.: CPSS LR-DDoS detection and defense in edge computing utilizing DCNN Q-learning. IEEE Access 8, 42120\u201342130 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2976706","journal-title":"IEEE Access"},{"key":"4744_CR20","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.comnet.2019.01.031","volume":"152","author":"Z Wu","year":"2019","unstructured":"Wu, Z., Pan, Q., Yue, M., Liu, L.: Sequence alignment detection of TCP-targeted synchronous low-rate DoS attacks. Comput. Netw. 152, 64\u201377 (2019). https:\/\/doi.org\/10.1016\/j.comnet.2019.01.031","journal-title":"Comput. Netw."},{"key":"4744_CR21","doi-asserted-by":"publisher","unstructured":"Zhang, D., Tang, D., Tang, L., Dai, R., Chen, J., Zhu, N.: PCA-SVM-based approach of detecting low-rate DoS attack. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), pp. 1163\u20131170 (2019). https:\/\/doi.org\/10.1109\/HPCC\/SmartCity\/DSS.2019.00164","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00164"},{"key":"4744_CR22","doi-asserted-by":"publisher","unstructured":"Yan, Y., Tang, D., Zhan, S., Dai, R., Chen, J., Zhu, N.: Low-rate DoS attack detection based on improved logistic regression. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), pp. 468\u2013476 (2019). https:\/\/doi.org\/10.1109\/HPCC\/SmartCity\/DSS.2019.00076","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00076"},{"key":"4744_CR23","doi-asserted-by":"publisher","unstructured":"Du, Z., Ma, L., Li, H., Li, Q., Sun, G., Liu, Z.: Network traffic anomaly detection based on wavelet analysis. In: 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 94\u2013101 (2018). https:\/\/doi.org\/10.1109\/SERA.2018.8477230","DOI":"10.1109\/SERA.2018.8477230"},{"key":"4744_CR24","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ipl.2018.06.001","volume":"138","author":"N Agrawal","year":"2018","unstructured":"Agrawal, N., Tapaswi, S.: Low rate cloud DDoS attack defense method based on power spectral density analysis. Inf. Process. Lett. 138, 44\u201350 (2018). https:\/\/doi.org\/10.1016\/j.ipl.2018.06.001","journal-title":"Inf. Process. Lett."},{"issue":"3.24","key":"4744_CR25","first-page":"479","volume":"7","author":"R Panigrahi","year":"2018","unstructured":"Panigrahi, R., Borah, S.: A detailed analysis of CICIDS2017 dataset for designing intrusion detection systems. Int. J. Eng. Technol. 7(3.24), 479\u2013482 (2018)","journal-title":"Int. J. Eng. Technol."},{"issue":"2","key":"4744_CR26","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1109\/TNSM.2020.3014929","volume":"18","author":"M Injadat","year":"2020","unstructured":"Injadat, M., Moubayed, A., Nassif, A.B., Shami, A.: Multi-stage optimized machine learning framework for network intrusion detection. IEEE Trans. Netw. Serv. Manag. 18(2), 1803\u20131816 (2020)","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"4744_CR27","first-page":"290","volume":"14","author":"F Marxabo","year":"2022","unstructured":"Marxabo, F., et al.: A detailed analysis of the KDD CUP 99 data set. Eurasian Res. Bull. 14, 290\u2013300 (2022)","journal-title":"Eurasian Res. Bull."},{"key":"4744_CR28","doi-asserted-by":"crossref","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), pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"4744_CR29","doi-asserted-by":"crossref","unstructured":"Creech, G., Hu, J.: Generation of a new IDS test dataset: time to retire the KDD collection. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC), pp. 4487\u20134492. IEEE (2013)","DOI":"10.1109\/WCNC.2013.6555301"},{"key":"4744_CR30","first-page":"108","volume":"1","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1, 108\u2013116 (2018)","journal-title":"ICISSp"},{"key":"4744_CR31","doi-asserted-by":"publisher","first-page":"37131","DOI":"10.1109\/ACCESS.2023.3266979","volume":"11","author":"V Hnamte","year":"2023","unstructured":"Hnamte, V., Nhung-Nguyen, H., Hussain, J., Hwa-Kim, Y.: A novel two-stage deep learning model for network intrusion detection: LSTM-AE. IEEE Access 11, 37131\u201337148 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3266979","journal-title":"IEEE Access"},{"key":"4744_CR32","doi-asserted-by":"publisher","first-page":"9965","DOI":"10.1007\/s13369-021-06484-9","volume":"47","author":"A Prasad","year":"2022","unstructured":"Prasad, A., Chandra, S.: VMFCVD: an optimized framework to combat volumetric DDoS attacks using machine learning. Arab. J. Sci. Eng. 47, 9965\u20139983 (2022). https:\/\/doi.org\/10.1007\/s13369-021-06484-9","journal-title":"Arab. J. Sci. Eng."},{"key":"4744_CR33","doi-asserted-by":"crossref","unstructured":"Luong, M., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. CoRR. http:\/\/arxiv.org\/abs\/1508.04025 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"key":"4744_CR34","doi-asserted-by":"publisher","DOI":"10.3390\/computers10070088","author":"C-W Tien","year":"2021","unstructured":"Tien, C.-W., Huang, T.-Y., Chen, P.-C., Wang, J.-H.: Using autoencoders for anomaly detection and transfer learning in IoT. Computers (2021). https:\/\/doi.org\/10.3390\/computers10070088","journal-title":"Computers"},{"key":"4744_CR35","unstructured":"Agarap, A.F.: Deep learning using rectified linear units (ReLU). CoRR. http:\/\/arxiv.org\/abs\/1803.08375 (2018)"},{"key":"4744_CR36","doi-asserted-by":"publisher","first-page":"181916","DOI":"10.1109\/ACCESS.2020.3028690","volume":"8","author":"A Bhardwaj","year":"2020","unstructured":"Bhardwaj, A., Mangat, V., Vig, R.: Hyperband tuned deep neural network with well posed stacked sparse autoencoder for detection of DDoS attacks in cloud. IEEE Access 8, 181916\u2013181929 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3028690","journal-title":"IEEE Access"},{"key":"4744_CR37","doi-asserted-by":"publisher","first-page":"107042","DOI":"10.1016\/j.comnet.2019.107042","volume":"168","author":"W Elmasry","year":"2020","unstructured":"Elmasry, W., Akbulut, A., Zaim, A.H.: Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Comput. Netw. 168, 107042 (2020). https:\/\/doi.org\/10.1016\/j.comnet.2019.107042","journal-title":"Comput. Netw."},{"key":"4744_CR38","doi-asserted-by":"publisher","first-page":"105980","DOI":"10.1016\/j.asoc.2019.105980","volume":"87","author":"M Prasad","year":"2020","unstructured":"Prasad, M., Tripathi, S., Dahal, K.: An efficient feature selection based Bayesian and rough set approach for intrusion detection. Appl. Soft Comput. 87, 105980 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2019.105980","journal-title":"Appl. Soft Comput."},{"key":"4744_CR39","doi-asserted-by":"publisher","first-page":"102062","DOI":"10.1016\/j.cose.2020.102062","volume":"99","author":"M Prasad","year":"2020","unstructured":"Prasad, M., Tripathi, S., Dahal, K.: Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection. Comput. Secur. 99, 102062 (2020). https:\/\/doi.org\/10.1016\/j.cose.2020.102062","journal-title":"Comput. Secur."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04744-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04744-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04744-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T16:46:11Z","timestamp":1743353171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04744-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,26]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["4744"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04744-5","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,26]]},"assertion":[{"value":"28 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","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 that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"103"}}