{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:34:45Z","timestamp":1772120085550,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s13042-025-02617-w","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T02:03:47Z","timestamp":1748657027000},"page":"6111-6134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial immune based intrusion detection and mitigation system using entropy fluctuation method and deep maxout classifier"],"prefix":"10.1007","volume":"16","author":[{"given":"Pallavi","family":"Chitte","sequence":"first","affiliation":[]},{"given":"Sangita","family":"Chaudhari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"key":"2617_CR1","volume":"58","author":"E Farzadnia","year":"2021","unstructured":"Farzadnia E, Shirazi H, Nowroozi A (2021) A novel sophisticated hybrid method for intrusion detection using the artificial immune system. J Inf Secur Appl 58:102721","journal-title":"J Inf Secur Appl"},{"issue":"2","key":"2617_CR2","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s00521-020-05049-5","volume":"33","author":"B Yang","year":"2021","unstructured":"Yang B, Yang M (2021) Data-driven network layer security detection model and simulation for the Internet of Things based on an artificial immune system. Neural Comput Appl 33(2):655\u201366","journal-title":"Neural Comput Appl"},{"issue":"2","key":"2617_CR3","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.11591\/ijece.v11i2.pp1666-1674","volume":"11","author":"FA Al-Ibraheemi","year":"2021","unstructured":"Al-Ibraheemi FA, Al-Ibraheemi S, Amintoosi H (2021) A hybrid method of genetic algorithm and support vector machine for DNS tunneling detection. Int J Electric Comput Eng 11(2):1666","journal-title":"Int J Electric Comput Eng"},{"key":"2617_CR4","doi-asserted-by":"publisher","unstructured":"Alzubi JA, Alzubi OA, Qiqieh I, Singh A (2024) A blended deep learning intrusion detection framework for consumable edge-centric IoMT industry. IEEE Trans Consum Electron 70(1):2049\u20132057. https:\/\/doi.org\/10.1109\/TCE.2024.3350231, https:\/\/doi.org\/10.1007\/s10586-022-03686-0","DOI":"10.1109\/TCE.2024.3350231"},{"issue":"1","key":"2617_CR5","first-page":"1","volume":"1","author":"TM Chen","year":"2014","unstructured":"Chen TM, Blasco J, Alzubi J, Alzubi O (2014) Intrusion detection. Eng Technol Ref 1(1):1\u20139","journal-title":"Eng Technol Ref"},{"issue":"16","key":"2617_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5815\/ijcnis.2024.03.01","volume":"2","author":"R Yadawad","year":"2024","unstructured":"Yadawad R, Kulkarni U, Jafar A (2024) Auto-metric graph neural network for attack detection on IoT-based smart environment and secure data transmission using advanced wild horse standard encryption method. Int J Comput Netw Inf Secur 2(16):1\u201315. https:\/\/doi.org\/10.5815\/ijcnis.2024.03.01","journal-title":"Int J Comput Netw Inf Secur"},{"issue":"1","key":"2617_CR7","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s42454-020-00017-9","volume":"3","author":"J Brown","year":"2021","unstructured":"Brown J, Anwar M (2021) Blacksite: human-in-the-loop artificial immune system for intrusion detection in internet of things. Human-Intell Syst Integr 3(1):55\u201367","journal-title":"Human-Intell Syst Integr"},{"issue":"2","key":"2617_CR8","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/s12065-019-00340-4","volume":"14","author":"DJ Prathyusha","year":"2021","unstructured":"Prathyusha DJ, Kannayaram G (2021) A cognitive mechanism for mitigating DDoS attacks using the artificial immune system in a cloud environment. Evol Intel 14(2):607\u201318","journal-title":"Evol Intel"},{"key":"2617_CR9","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1007\/s10586-022-03686-0","volume":"26","author":"OA Alzubi","year":"2023","unstructured":"Alzubi OA, Qiqieh I, Alzubi JA (2023) Fusion of deep learning based cyberattack detection and classification model for intelligent systems. Cluster Comput 26:1363\u20131374","journal-title":"Cluster Comput"},{"issue":"9","key":"2617_CR10","doi-asserted-by":"publisher","first-page":"3565","DOI":"10.1007\/s12206-020-0808-x","volume":"34","author":"Q Jiang","year":"2020","unstructured":"Jiang Q, Chang F (2020) A novel antibody population optimization-based artificial immune system for rotating equipment anomaly detection. J Mech Sci Technol 34(9):3565\u201374","journal-title":"J Mech Sci Technol"},{"key":"2617_CR11","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1007\/s10586-021-03459-1","volume":"25","author":"OA Alzubi","year":"2022","unstructured":"Alzubi OA, Alzubi JA, Al-Zoubi AM et al (2022) An efficient malware detection approach with feature weighting based on Harris Hawks optimization. Cluster Comput 25:2369\u20132387. https:\/\/doi.org\/10.1007\/s10586-021-03459-1","journal-title":"Cluster Comput"},{"issue":"4","key":"2617_CR12","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 (2020) 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","journal-title":"Clust Comput"},{"issue":"3","key":"2617_CR13","first-page":"219","volume":"44","author":"S Dwivedi","year":"2022","unstructured":"Dwivedi S, Vardhan M, Tripathi S (2022) Defense against distributed DoS attack detection by using intelligent evolutionary algorithm. Int J Comput Appl 44(3):219\u2013229","journal-title":"Int J Comput Appl"},{"key":"2617_CR14","doi-asserted-by":"publisher","first-page":"2529","DOI":"10.1007\/s11276-021-02604-3","volume":"27","author":"P Verma","year":"2021","unstructured":"Verma P, Tapaswi S, Godfrey WW (2021) Service governance and isolation-based approach to mitigate internal collateral damages in the cloud caused by DDoS attacks. Wireless Netw 27:2529\u20132548","journal-title":"Wireless Netw"},{"issue":"1","key":"2617_CR15","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/TSC.2017.2694426","volume":"13","author":"OA Wahab","year":"2020","unstructured":"Wahab OA, Bentahar J, Otrok H, Mourad A (2020) Optimal load distribution for the detection of VM-based DDoS attacks in the cloud. IEEE Trans Serv Comput 13(1):114\u2013129. https:\/\/doi.org\/10.1109\/TSC.2017.2694426","journal-title":"IEEE Trans Serv Comput"},{"key":"2617_CR16","doi-asserted-by":"publisher","first-page":"152300","DOI":"10.1109\/ACCESS.2021.3126535","volume":"9","author":"M Nadeem","year":"2021","unstructured":"Nadeem M, Arshad A, Riaz S, Band SS, Mosavi A (2021) Intercept the cloud network from brute force and DDoS attacks via intrusion detection and prevention systems. IEEE Access 9:152300\u2013152309. https:\/\/doi.org\/10.1109\/ACCESS.2021.3126535","journal-title":"IEEE Access"},{"key":"2617_CR17","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 (2020) Hyperband-tuned deep neural network with well-posed stacked sparse autoencoder for detection of DDoS attacks in the cloud. IEEE Access 8:181916\u2013181929. https:\/\/doi.org\/10.1109\/ACCESS.2020.3028690","journal-title":"IEEE Access"},{"key":"2617_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102260","volume":"105","author":"GS Kushwah","year":"2021","unstructured":"Kushwah GS, Ranga V (2021) Optimized extreme learning machine for detecting DDoS attacks in cloud computing. Comput Secur 105:102260","journal-title":"Comput Secur"},{"key":"2617_CR19","doi-asserted-by":"crossref","unstructured":"Agarwal A, Khari M, Singh R (2022) Detection of DDOS attack using deep learning model in cloud storage application. Wireless Person Commun 1\u201321","DOI":"10.1007\/s11277-021-08271-z"},{"issue":"2","key":"2617_CR20","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s12065-019-00244-","volume":"14","author":"Q Penukonda Shaik Mohammed","year":"2021","unstructured":"Penukonda Shaik Mohammed Q, Paramasivam I (2021) Design and analysis of behavior-based DDoS detection algorithm for data centers in the cloud. Evolut Intell 14(2):395\u2013404. https:\/\/doi.org\/10.1007\/s12065-019-00244-","journal-title":"Evolut Intell"},{"key":"2617_CR21","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1016\/j.procs.2020.03.282","volume":"167","author":"KB Virupakshar","year":"2020","unstructured":"Virupakshar KB, Asundi M, Channal K, Shettar P, Patil S, Narayan DG (2020) Distributed Denial of Service (DDoS) attacks detection system for OpenStack-based private cloud. Procedia Comput Sci 167:2297\u20132307","journal-title":"Procedia Comput Sci"},{"issue":"5","key":"2617_CR22","doi-asserted-by":"publisher","first-page":"4889","DOI":"10.1007\/s12652-020-01916-0","volume":"12","author":"A Punitha","year":"2021","unstructured":"Punitha A, Indumathi G (2021) RETRACTED ARTICLE: a novel centralized cloud information accountability integrity with ensemble neural network-based attack detection approach for cloud data. J Ambient Intell Humaniz Comput 12(5):4889\u20134900","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2617_CR23","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s42044-019-00039-w","volume":"2","author":"R Saxena","year":"2019","unstructured":"Saxena R, Dey S (2019) DDoS prevention using third party auditor in cloud computing. Iran J Comput Sci 2:231\u2013244","journal-title":"Iran J Comput Sci"},{"key":"2617_CR24","unstructured":"M NALAYINI C, Katiravan J (2022) Detection of DDoS attack using machine learning algorithms. Available at SSRN 4173187, 9(7)"},{"key":"2617_CR25","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1007\/s12652-018-0754-y","volume":"10","author":"H Pillutla","year":"2019","unstructured":"Pillutla H, Arjunan A (2019) Fuzzy self-organizing maps-based DDoS mitigation mechanism for software defined networking in cloud computing. J Ambient Intell Humaniz Comput 10:1547\u20131559","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"2","key":"2617_CR26","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3390\/fi15020076","volume":"15","author":"S Ahmed","year":"2023","unstructured":"Ahmed S, Khan ZA, Mohsin SM, Latif S, Aslam S, Mujlid H, Najam Z (2023) Effective and efficient DDoS attack detection using deep learning algorithm, multi-layer perceptron. Future Internet 15(2):76","journal-title":"Future Internet"},{"issue":"7","key":"2617_CR27","first-page":"659","volume":"44","author":"G Singh","year":"2022","unstructured":"Singh G, Khare N (2022) A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques. Int J Comput Appl 44(7):659\u2013669","journal-title":"Int J Comput Appl"},{"issue":"7","key":"2617_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4069","volume":"32","author":"JA Alzubi","year":"2021","unstructured":"Alzubi JA (2021) Bipolar fully recurrent deep structured neural learning-based attack detection for securing industrial sensor networks. Trans Emerg Telecommun Technol 32(7):e4069","journal-title":"Trans Emerg Telecommun Technol"},{"key":"2617_CR29","unstructured":"Amami R, Ayed DB, Ellouze N (2015) Practical selection of SVM supervised parameters with different feature representations for vowel recognition. arXiv preprint arXiv:1507.06020"},{"issue":"4","key":"2617_CR30","doi-asserted-by":"publisher","first-page":"771","DOI":"10.5755\/j01.itc.51.4.31818","volume":"51","author":"A Padmashree","year":"2022","unstructured":"Padmashree A, Krishnamoorthi M (2022) Decision tree with Pearson correlation-based recursive feature elimination model for attack detection in IoT environment. Inf Technol Control 51(4):771\u2013785","journal-title":"Inf Technol Control"},{"issue":"9","key":"2617_CR31","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1109\/TPAMI.2006.186","volume":"28","author":"KE Hild","year":"2006","unstructured":"Hild KE, Erdogmus D, Torkkola K, Principe JC (2006) Feature extraction using information-theoretic learning. IEEE Trans Pattern Anal Mach Intell 28(9):1385\u20131392","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"24","key":"2617_CR32","doi-asserted-by":"publisher","first-page":"4137","DOI":"10.3390\/electronics11244137","volume":"11","author":"J Peta","year":"2022","unstructured":"Peta J, Koppu S (2022) An IoT-based framework and ensemble optimized deep maxout network model for breast cancer classification. Electronics 11(24):4137","journal-title":"Electronics"},{"issue":"1","key":"2617_CR33","first-page":"6974993","volume":"2022","author":"J Yi","year":"2022","unstructured":"Yi J (2022) Deep learning in data mining management of industrial and commercial enterprises. Mob Inf Syst 2022(1):6974993","journal-title":"Mob Inf Syst"},{"key":"2617_CR34","unstructured":"https:\/\/www.unb.ca\/cic\/datasets\/ddos-2019.html"},{"key":"2617_CR35","unstructured":"https:\/\/research.unsw.edu.au\/projects\/unsw-nb15-dataset"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02617-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02617-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02617-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:01:25Z","timestamp":1757156485000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02617-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,31]]},"references-count":35,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2617"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02617-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4852188\/v1","asserted-by":"object"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,31]]},"assertion":[{"value":"3 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2025","order":3,"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"}}]}}