{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:49:05Z","timestamp":1776710945795,"version":"3.51.2"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-026-08400-0","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:01:42Z","timestamp":1776708102000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A decision-level hybrid VGG\u2013fuzzy framework for robust and interpretable DDoS attack detection in network traffic"],"prefix":"10.1007","volume":"82","author":[{"given":"Roya","family":"Jamaati Gashti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehdi","family":"Sadeghzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alireza","family":"TamjidYamcholo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ladan","family":"Riazi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"8400_CR1","doi-asserted-by":"crossref","unstructured":"Patel J, Katkar V (2016) a multi-classifier based novel DoS\/DDoS attack detection using fuzzy logic, Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing","DOI":"10.1007\/978-981-10-0135-2_77"},{"key":"8400_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103511","author":"DM Sharif","year":"2023","unstructured":"Sharif DM, Beitollahi H (2023) Detection of application-layer DDoS attacks using machine learning and genetic algorithms. Comput Secur. https:\/\/doi.org\/10.1016\/j.cose.2023.103511","journal-title":"Comput Secur"},{"key":"8400_CR3","doi-asserted-by":"crossref","unstructured":"Ozcelik I, Brooks R (2020) Distributed Denial of Service Attacks: Real-world Detection and Mitigation. Chapman and Hall Book, CRC Press, Taylor & Francis Group","DOI":"10.1201\/9781315213125"},{"key":"8400_CR4","doi-asserted-by":"publisher","unstructured":"Tomar K, Bisht K, Joshi K, Katarya R (2023) Cyber Attack Detection in IoT using Deep Learning Techniques. 6th International Conference on Information Systems and Computer Networks. https:\/\/doi.org\/10.1109\/ISCON57294.2023.10111990","DOI":"10.1109\/ISCON57294.2023.10111990"},{"key":"8400_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103962","author":"MB Anley","year":"2024","unstructured":"Anley MB, Genovese A, Agostinello D, Piuri V (2024) Robust DDoS attack detection with adaptive transfer learning. Comput Secur. https:\/\/doi.org\/10.1016\/j.cose.2024.103962","journal-title":"Comput Secur"},{"key":"8400_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-024-05994-1","author":"HS Sharma","year":"2024","unstructured":"Sharma HS, Singh KJ (2024) Intrusion detection system: a deep neural network-based concatenated approach. J Supercomput. https:\/\/doi.org\/10.1007\/s11227-024-05994-1","journal-title":"J Supercomput"},{"key":"8400_CR7","volume-title":"Fuzzy Logic","author":"LA Zadeh","year":"2009","unstructured":"Zadeh LA (2009) Fuzzy Logic. Springer-Verlag"},{"issue":"22","key":"8400_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11223665","volume":"11","author":"A Alzahrani","year":"2022","unstructured":"Alzahrani A, Ayadi M, Asiri M, Al-Rasheed A, Ksibi A (2022) Detecting the presence of malware and identifying the type of cyber attack using deep learning and VGG16 techniques. Electronics 11(22):3665. https:\/\/doi.org\/10.3390\/electronics11223665","journal-title":"Electronics"},{"key":"8400_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107792","author":"VM Rios","year":"2021","unstructured":"Rios VM, Inacio PRM, Magoni D, Freire MM (2021) Detection of reduction-of-quality DDoS attacks using fuzzy logic and machine learning algorithms. Comput Netw. https:\/\/doi.org\/10.1016\/j.comnet.2020.107792","journal-title":"Comput Netw"},{"key":"8400_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-024-09144-w","author":"P Shukla","year":"2024","unstructured":"Shukla P, Krishna CR, Patil NV (2024) Distributed ensemble method using deep learning to detect DDoS attacks in IoT networks. Arab J Sci Eng. https:\/\/doi.org\/10.1007\/s13369-024-09144-w","journal-title":"Arab J Sci Eng"},{"key":"8400_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-023-11885-4","author":"SS Shankar","year":"2024","unstructured":"Shankar SS, Hung BT, Chakrabarti P, Chakrabarti T, Parasa G (2024) A novel optimization based deep learning with artificial intelligence approach to detect intrusion attack in network system. Educ Inf Technol. https:\/\/doi.org\/10.1007\/s10639-023-11885-4","journal-title":"Educ Inf Technol"},{"key":"8400_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103560","author":"UK Lilhore","year":"2023","unstructured":"Lilhore UK, Dalal S, Simaiya S (2023) A cognitive security framework for detecting intrusions in IoT and 5G utilizing deep learning. Comput Secur. https:\/\/doi.org\/10.1016\/j.cose.2023.103560","journal-title":"Comput Secur"},{"key":"8400_CR13","doi-asserted-by":"publisher","unstructured":"Araki R, Hsu Y, Matsuoka M (2022) Early Detection of Campus Network DDoS Attacks using Predictive Models. IEEE Global Communications Conference: Communication & Information Systems Security. https:\/\/doi.org\/10.1109\/GLOBECOM48099.2022.10000974","DOI":"10.1109\/GLOBECOM48099.2022.10000974"},{"key":"8400_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12173731","author":"SK Gebresilassie","year":"2023","unstructured":"Gebresilassie SK, Rafferty J, Chen L, Cui Z, Abu-Tair M (2023) Transfer and CNN-based de-authentication (disassociation), DoS attack detection in IoT Wi-Fi networks. Electronics (Basel). https:\/\/doi.org\/10.3390\/electronics12173731","journal-title":"Electronics (Basel)"},{"key":"8400_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103385","author":"SH Khan","year":"2023","unstructured":"Khan SH, Alahmadi TJ, Ullah W, Iqbal J, Rahim A, KAlkahtani H, Alghamdi W, Almagrabi AO (2023) A new deep boosted CNN and ensemble learning based IoT malware detection. Comput Secur. https:\/\/doi.org\/10.1016\/j.cose.2023.103385","journal-title":"Comput Secur"},{"issue":"5","key":"8400_CR16","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2023.0140580","volume":"14","author":"R Bingu","year":"2023","unstructured":"Bingu R, Jothilakshmi S (2023) Design of intrusion detection system using ensemble learning technique in cloud computing environment. Int J Adv Comput Sci Appl 14(5):0140580. https:\/\/doi.org\/10.14569\/IJACSA.2023.0140580","journal-title":"Int J Adv Comput Sci Appl"},{"key":"8400_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/s23042171","author":"Y Wang","year":"2023","unstructured":"Wang Y, Houng Y, Chen H, Tseng S (2023) Network anomaly intrusion detection based on deep learning approach. Sensors (Basel). https:\/\/doi.org\/10.3390\/s23042171","journal-title":"Sensors (Basel)"},{"key":"8400_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05197-0","author":"O Elnakib","year":"2023","unstructured":"Elnakib O, Shaaban E, Mahmoud M, Emara K (2023) EIDM: deep learning model for IoT intrusion detection systems. J Supercomput. https:\/\/doi.org\/10.1007\/s11227-023-05197-0","journal-title":"J Supercomput"},{"key":"8400_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109277","author":"A Ahmad Najar","year":"2024","unstructured":"Ahmad Najar A, Naik SM (2024) A robust DDoS intrusion detection system using convolutional neural network. Comput Electr Eng. https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109277","journal-title":"Comput Electr Eng"},{"key":"8400_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2024.102777","author":"A Nazir","year":"2024","unstructured":"Nazir A, He J, Zhu N, Qureshi SS, Qureshi SU, Ullah F, Wajahat A, Pathan MS (2024) A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem. Ain Shams Eng J. https:\/\/doi.org\/10.1016\/j.asej.2024.102777","journal-title":"Ain Shams Eng J"},{"issue":"5","key":"8400_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/s25051346","volume":"25","author":"N Ain","year":"2025","unstructured":"Ain N, Sardaraz M, Tahir M, Elsoud M, Alourani A (2025) Securing IoT networks against DDoS attacks: A hybrid deep learning approach. Sensors Journal 25(5):1346. https:\/\/doi.org\/10.3390\/s25051346","journal-title":"Sensors Journal"},{"key":"8400_CR22","doi-asserted-by":"publisher","unstructured":"Reza F (2024) DDoS-Net: Classifying DDoS Attacks in Wireless Sensor Networks with Hybrid Deep Learning. 6th International Conference on Electrical Engineering and Information & Communication Technology. https:\/\/doi.org\/10.1109\/ICEEICT62016.2024.10534545","DOI":"10.1109\/ICEEICT62016.2024.10534545"},{"key":"8400_CR23","unstructured":"Zhang L, Liu W, Yang S (2023) A hybrid CNN\u2013BiLSTM architecture with attention for DDoS intrusion detection. Neural Computing and Applications"},{"key":"8400_CR24","unstructured":"Nguyen P, Tran Q (2024) Deep learning-based DDoS attack detection with CNN and sequential LSTM models. Journal of Network and Computer Applications"},{"key":"8400_CR25","unstructured":"Kim Y, Lee M (2025) Attention\u2011augmented deep learning for network intrusion classification. Applied Soft Computing"},{"issue":"2","key":"8400_CR26","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s11227-024-05000-x","volume":"81","author":"S Patel","year":"2025","unstructured":"Patel S, Singh R (2025) Ensemble CNN\u2013LSTM model with feature selection for IoT DDoS detection. J Supercomput 81(2):437\u2013452. https:\/\/doi.org\/10.1007\/s11227-024-05000-x","journal-title":"J Supercomput"},{"key":"8400_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103661","author":"J Lee","year":"2023","unstructured":"Lee J, Park H (2023) Information-theory feature selection in hybrid deep models for intrusion detection. Comput Secur. https:\/\/doi.org\/10.1016\/j.cose.2023.103661","journal-title":"Comput Secur"},{"key":"8400_CR28","unstructured":"Ahmed Z, Rahman A (2024) Feature selection assisted deep learning for robust DDoS classification. Security and Communication Networks"},{"key":"8400_CR29","unstructured":"Ali T, Khan F (2023) Hybrid CNN + SVM ensemble for intrusion detection: A performance comparison. Sensors, Volume 23, Issue 9"},{"key":"8400_CR30","unstructured":"Chen R, Yu C (2022) Deep feature extraction with classical classifiers for network security. International Journal of Network Management, Volume 32, Issue 10"},{"key":"8400_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/app13031484","author":"I Atacak","year":"2023","unstructured":"Atacak I (2023) An ensemble approach based on fuzzy logic using machine learning classifiers for Android malware detection. Applied Sciences Journal. https:\/\/doi.org\/10.3390\/app13031484","journal-title":"Applied Sciences Journal"},{"key":"8400_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108466","author":"H Lin","year":"2022","unstructured":"Lin H, Wu C, Masdari M (2022) A comprehensive survey of network traffic anomalies and DDoS attacks detection schemes using fuzzy techniques. Computers and Electrical Engineering Journal. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108466","journal-title":"Computers and Electrical Engineering Journal"},{"issue":"3","key":"8400_CR33","first-page":"987","volume":"39","author":"S Garcia","year":"2023","unstructured":"Garcia S, Fernandez A (2023) Fuzzy logic assisted intrusion detection: challenges and opportunities. Comput Intell 39(3):987\u20131002","journal-title":"Comput Intell"},{"issue":"4","key":"8400_CR34","first-page":"455","volume":"20","author":"V Singh","year":"2025","unstructured":"Singh V, Kaur P (2025) Fuzzy clustering with machine learning for intrusion detection: a comparative survey. Int J Inf Secur 20(4):455\u2013470","journal-title":"Int J Inf Secur"},{"key":"8400_CR35","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-06719-x","volume":"15","author":"G Usha","year":"2025","unstructured":"Usha G, Karthikeyan H, Gautam K (2025) DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system. Sci Rep 15:20597. https:\/\/doi.org\/10.1038\/s41598-025-06719-x","journal-title":"Sci Rep"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08400-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08400-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08400-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:01:46Z","timestamp":1776708106000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08400-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["8400"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08400-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"25 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2026","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"364"}}