{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:39:03Z","timestamp":1757623143689,"version":"3.44.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100019725","name":"Deanship of Scientific Research, Prince Sattam bin Abdulaziz University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07715-8","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T12:01:03Z","timestamp":1755259263000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Implementing a hybrid deep learning technique for detecting malicious DNS over HTTPS (DoH) traffic"],"prefix":"10.1007","volume":"81","author":[{"given":"Mohemmed","family":"Sha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adel","family":"Binbusayyis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"7715_CR1","doi-asserted-by":"crossref","unstructured":"Vanhoenshoven F, Napoles G, Falcon R, Vanhoof K, Koppen M (2016) \u201cDetecting malicious URLs using machine learning techniques,\u201d In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece: IEEE, Dec. 2016, pp. 1\u20138.","DOI":"10.1109\/SSCI.2016.7850079"},{"key":"7715_CR2","unstructured":"\u201cInternet of Things beyond the Hype: Research, Innovation and Deploymen.\u201d Accessed: Oct. 02, 2024."},{"key":"7715_CR3","doi-asserted-by":"crossref","unstructured":"Chen J, Piuri V, Su C, Yung M, Eds., Network and System Security, vol. 9955. In: Lecture Notes in Computer Science, vol. 9955. Cham: Springer International Publishing, 2016.","DOI":"10.1007\/978-3-319-46298-1"},{"key":"7715_CR4","unstructured":"Sahoo D, Liu C, Hoi SCH (2019) \u201cMalicious URL Detection using Machine Learning: A Survey\u201d"},{"key":"7715_CR5","unstructured":"Saxe J, Berlin K (2017) \u201ceXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys,\u201d"},{"key":"7715_CR6","doi-asserted-by":"publisher","first-page":"108043","DOI":"10.1016\/j.compeleceng.2022.108043","volume":"101","author":"V Bharathiand","year":"2022","unstructured":"Bharathiand V, Vinoth Kumar CNS (2022) A real time health care cyber attack detection using ensemble classifier. Comput Electr Eng 101:108043","journal-title":"Comput Electr Eng"},{"issue":"3","key":"7715_CR7","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.istr.2005.07.001","volume":"10","author":"T Pietraszek","year":"2005","unstructured":"Pietraszek T, Tanner A (2005) Data mining and machine learning\u2014towards reducing false positives in intrusion detection. Inf Secur Tech Rep 10(3):169\u2013183","journal-title":"Inf Secur Tech Rep"},{"issue":"5","key":"7715_CR8","first-page":"1","volume":"120","author":"S Ranveer","year":"2015","unstructured":"Ranveer S, Hiray S (2015) Comparative analysis of feature extraction methods of malware detection. Int J Comput Appl 120(5):1\u20137","journal-title":"Int J Comput Appl"},{"key":"7715_CR9","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.jpdc.2020.03.012","volume":"141","author":"G Xiao","year":"2020","unstructured":"Xiao G, Li J, Chen Y, Li K (2020) MalFCS: an effective malware classification framework with automated feature extraction based on deep convolutional neural networks. J Parallel Distrib Comput 141:49\u201358","journal-title":"J Parallel Distrib Comput"},{"issue":"no. 4","key":"7715_CR10","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1587\/transinf.2015CYP0005","volume":"E99.D","author":"W Zhang","year":"2016","unstructured":"Zhang W, Ren H, Jiang Q (2016) Application of feature engineering for phishing detection. IEICE Trans Inf Syst E99.D(4):1062\u20131070","journal-title":"IEICE Trans Inf Syst"},{"issue":"2","key":"7715_CR11","doi-asserted-by":"publisher","first-page":"239","DOI":"10.3390\/app9020239","volume":"9","author":"B Ndibanje","year":"2019","unstructured":"Ndibanje B, Kim KH, Kang YJ, Kim HH, Kim TY, Lee HJ (2019) Cross-method-based analysis and classification of malicious behavior by API calls extraction. Appl Sci 9(2):239","journal-title":"Appl Sci"},{"key":"7715_CR12","doi-asserted-by":"publisher","first-page":"206303","DOI":"10.1109\/ACCESS.2020.3036491","volume":"8","author":"SA Roseline","year":"2020","unstructured":"Roseline SA, Geetha S, Kadry S, Nam Y (2020) Intelligent vision-based malware detection and classification using deep random forest paradigm. IEEE Access 8:206303\u2013206324","journal-title":"IEEE Access"},{"issue":"2","key":"7715_CR13","doi-asserted-by":"publisher","first-page":"182","DOI":"10.3390\/e23020182","volume":"23","author":"S Kumi","year":"2021","unstructured":"Kumi S, Lim C, Lee SG (2021) Malicious URL detection based on associative classification. Entropy 23(2):182","journal-title":"Entropy"},{"issue":"5","key":"7715_CR14","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.3390\/s20051452","volume":"20","author":"M Gao","year":"2020","unstructured":"Gao M, Ma L, Liu H, Zhang Z, Ning Z, Xu J (2020) Malicious network traffic detection based on deep neural networks and association analysis. Sensors 20(5):1452","journal-title":"Sensors"},{"key":"7715_CR15","doi-asserted-by":"publisher","first-page":"97258","DOI":"10.1109\/ACCESS.2020.2995157","volume":"8","author":"D Liu","year":"2020","unstructured":"Liu D, Lee JH (2020) CNN based malicious website detection by invalidating multiple web spams. IEEE Access 8:97258\u201397266","journal-title":"IEEE Access"},{"key":"7715_CR16","doi-asserted-by":"crossref","unstructured":"Guan Z, Wang J, Wang X, Xin W, Cui J, Jing X (2021) \u201cA Comparative Study of RNN-based Methods for Web Malicious Code Detection,\u201d In: 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), Chengdu, China: IEEE, 769\u2013773.","DOI":"10.1109\/ICCCS52626.2021.9449245"},{"issue":"1","key":"7715_CR17","doi-asserted-by":"publisher","first-page":"997","DOI":"10.11591\/ijece.v10i1.pp997-1005","volume":"10","author":"F Khan","year":"2020","unstructured":"Khan F, Ahamed J, Kadry S, Ramasamy LK (2020) Detecting malicious URLs using binary classification through adaboost algorithm. Int J Electric Comput Eng (IJECE) 10(1):997","journal-title":"Int J Electric Comput Eng (IJECE)"},{"key":"7715_CR18","doi-asserted-by":"crossref","unstructured":"Uppal D, Sinha R, Mehra V, Jain V (2014) \u201cMalware detection and classification based on extraction of API sequences,\u201d in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India: IEEE, 2337\u20132342.","DOI":"10.1109\/ICACCI.2014.6968547"},{"key":"7715_CR19","doi-asserted-by":"publisher","first-page":"148853","DOI":"10.1109\/ACCESS.2019.2946482","volume":"7","author":"H Yang","year":"2019","unstructured":"Yang H, Li S, Wu X, Lu H, Han W (2019) A novel solutions for malicious code detection and family clustering based on machine learning. IEEE Access 7:148853\u2013148860","journal-title":"IEEE Access"},{"key":"7715_CR20","doi-asserted-by":"crossref","unstructured":"Xuan CD, Dinh H, Victor T (2020) \u201cMalicious URL Detection based on Machine Learning,\u201d Int J Adv Comput Sci Appl, 11(1)","DOI":"10.14569\/IJACSA.2020.0110119"},{"issue":"3","key":"7715_CR21","first-page":"1355","volume":"34","author":"R Vinayakumar","year":"2018","unstructured":"Vinayakumar R, Soman KP, Poornachandran P (2018) Detecting malicious domain names using deep learning approaches at scale. J Intell Fuzzy Syst 34(3):1355\u20131367","journal-title":"J Intell Fuzzy Syst"},{"key":"7715_CR22","doi-asserted-by":"publisher","first-page":"107360","DOI":"10.1016\/j.asoc.2021.107360","volume":"107","author":"L Ilias","year":"2021","unstructured":"Ilias L, Roussaki I (2021) Detecting malicious activity in Twitter using deep learning techniques. Appl Soft Comput 107:107360","journal-title":"Appl Soft Comput"},{"issue":"1","key":"7715_CR23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s40537-021-00475-1","volume":"8","author":"A Alshammari","year":"2021","unstructured":"Alshammari A, Aldribi A (2021) Apply machine learning techniques to detect malicious network traffic in cloud computing. J Big Data 8(1):90","journal-title":"J Big Data"},{"key":"7715_CR24","unstructured":"Li S, Cheng Y, Wang W, Liu Y, Chen T (2020) \u201cLearning to detect malicious clients for robust federated learning\u201d"},{"key":"7715_CR25","doi-asserted-by":"publisher","first-page":"28855","DOI":"10.1109\/ACCESS.2019.2901864","volume":"7","author":"P Shi","year":"2019","unstructured":"Shi P, Zhang Z, Choo KKR (2019) Detecting malicious social bots based on clickstream sequences. IEEE Access 7:28855\u201328862","journal-title":"IEEE Access"},{"issue":"1","key":"7715_CR26","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/TNSM.2020.2966951","volume":"17","author":"I Hafeez","year":"2020","unstructured":"Hafeez I, Antikainen M, Ding AY, Tarkoma S (2020) IoT-KEEPER: detecting malicious IoT network activity using online traffic analysis at the edge. IEEE Trans Netw Serv Manag 17(1):45\u201359","journal-title":"IEEE Trans Netw Serv Manag"},{"key":"7715_CR27","doi-asserted-by":"publisher","first-page":"102540","DOI":"10.1016\/j.cose.2021.102540","volume":"113","author":"Y Lai","year":"2022","unstructured":"Lai Y et al (2022) Identifying malicious nodes in wireless sensor networks based on correlation detection. Comput Secur 113:102540","journal-title":"Comput Secur"},{"key":"7715_CR28","doi-asserted-by":"publisher","first-page":"9464","DOI":"10.1109\/ACCESS.2021.3049625","volume":"9","author":"J Yuan","year":"2021","unstructured":"Yuan J, Chen G, Tian S, Pei X (2021) Malicious URL detection based on a parallel neural joint model. IEEE Access 9:9464\u20139472","journal-title":"IEEE Access"},{"issue":"9","key":"7715_CR29","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.3390\/s21093025","volume":"21","author":"F Hussain","year":"2021","unstructured":"Hussain F et al (2021) A framework for malicious traffic detection in IoT healthcare environment. Sensors 21(9):3025","journal-title":"Sensors"},{"issue":"2","key":"7715_CR30","first-page":"375","volume":"10","author":"AE Karrar","year":"2022","unstructured":"Karrar AE (2022) The effect of using data pre-processing by imputations in handling missing values. Indones J Electr Eng Informa (IJEEI) 10(2):375\u2013384","journal-title":"Indones J Electr Eng Informa (IJEEI)"},{"key":"7715_CR31","doi-asserted-by":"crossref","unstructured":"Walach J, Filzmoser P, Hron K (2018), \u201cData Normalization and Scaling: Consequences for the Analysis in Omics Sciences,\u201d in Comprehensive Analytical Chemistry, 82, Elsevier, 165\u2013196.","DOI":"10.1016\/bs.coac.2018.06.004"},{"issue":"10","key":"7715_CR32","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.3390\/e23101258","volume":"23","author":"T Al-Shehari","year":"2021","unstructured":"Al-Shehari T, Alsowail RA (2021) An insider data leakage detection using one-hot encoding, synthetic minority oversampling and machine learning techniques. Entropy 23(10):1258","journal-title":"Entropy"},{"key":"7715_CR33","unstructured":"\u201cCIC-DoHBrw2020.\u201d Accessed: Oct. 03, 2024."}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07715-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07715-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07715-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T02:55:47Z","timestamp":1757386547000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07715-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,15]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["7715"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07715-8","relation":{},"ISSN":["1573-0484"],"issn-type":[{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2025,8,15]]},"assertion":[{"value":"30 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"1241"}}