{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:58:49Z","timestamp":1760785129281,"version":"3.37.3"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"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":["Soft Comput"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00500-023-07949-9","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T14:02:48Z","timestamp":1680012168000},"page":"7191-7208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine learning interpretability meets TLS fingerprinting"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3860-5999","authenticated-orcid":false,"given":"Mahdi","family":"Jafari Siavoshani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amirhossein","family":"Khajehpour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amirmohammad Ziaei","family":"Bideh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amirali","family":"Gatmiri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Taheri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"7949_CR1","unstructured":"A browser automation framework and ecosystem. https:\/\/github.com\/SeleniumHQ\/selenium (2020)"},{"key":"7949_CR2","doi-asserted-by":"crossref","unstructured":"Aceto G, Ciuonzo D, Montieri A, Pescap\u00e9 A(2019) Mobile encrypted traffic classification using deep learning: Experimental evaluation, lessons learned, and challenges. IEEE Trans Netw Serv Manag","DOI":"10.23919\/TMA.2018.8506558"},{"key":"7949_CR3","doi-asserted-by":"crossref","unstructured":"Anderson B, McGrew D (2019) Tls beyond the browser: combining end host and network data to understand application behavior. In Proceedings of the Internet Measurement Conference, IMC \u201919, pp 379\u2013392, New York, NY, USA, Association for Computing Machinery","DOI":"10.1145\/3355369.3355601"},{"key":"7949_CR4","unstructured":"Aviram N, Schinzel S, Somorovsky J, Heninger N, Dankel M, Steube J, Valenta L, Adrian D, Halderman JA, Dukhovni V, K\u00e4sper E, Cohney Shaanan\u00a0N, Engels S, Paar C, Shavitt Y (2016) Drown: breaking tls using sslv2. In USENIX security symposium"},{"key":"7949_CR5","doi-asserted-by":"crossref","unstructured":"Bakhshi T, Ghita B (2016) On internet traffic classification: a two-phased machine learning approach. J Comput Netw Commun 2016","DOI":"10.1155\/2016\/2048302"},{"key":"7949_CR6","doi-asserted-by":"crossref","unstructured":"Barnes Richard, Thomson Martin, Pironti Alfredo, Langley Adam (2015) Deprecating secure sockets layer version 3.0. In IETF RFC 7568,","DOI":"10.17487\/RFC7568"},{"key":"7949_CR7","unstructured":"Bergstra JS, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for hyper-parameter optimization. Adv Neural Inform Process Syst pp 2546\u20132554"},{"issue":"6","key":"7949_CR8","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1016\/j.comnet.2010.12.004","volume":"55","author":"P Bermolen","year":"2011","unstructured":"Bermolen P, Mellia M, Meo M, Rossi D, Valenti S (2011) Abacus: accurate behavioral classification of p2p-tv traffic. Comput Netw 55(6):1394\u20131411","journal-title":"Comput Netw"},{"issue":"2","key":"7949_CR9","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1145\/1129582.1129589","volume":"36","author":"L Bernaille","year":"2006","unstructured":"Bernaille L, Teixeira R, Akodkenou I, Soule A, Salamatian K (2006) Traffic classification on the fly. SIGCOMM Comput Commun Rev 36(2):23\u201326","journal-title":"SIGCOMM Comput Commun Rev"},{"key":"7949_CR10","unstructured":"B\u00f6ck H, Somorovsky J, Young C (2018) Return of bleichenbacher\u2019s oracle threat (robot). In Proceedings of the 27th USENIX conference on security symposium, SEC\u201918, pages 817\u2013832, USA, USENIX Association"},{"issue":"1","key":"7949_CR11","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s13174-018-0087-2","volume":"9","author":"R Boutaba","year":"2018","unstructured":"Boutaba R, Salahuddin MA, Limam N, Ayoubi S, Shahriar N, Estrada-Solano F, Caicedo OM (2018) A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J Int Serv Appl 9(1):16","journal-title":"J Int Serv Appl"},{"key":"7949_CR12","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.comnet.2014.11.001","volume":"76","author":"T Bujlow","year":"2015","unstructured":"Bujlow T, Carela-Espa\u00f1ol V, Barlet-Ros P (2015) Independent comparison of popular dpi tools for traffic classification. Comput Netw 76:75\u201389","journal-title":"Comput Netw"},{"key":"7949_CR13","doi-asserted-by":"crossref","unstructured":"Cai X, Zhang XC, Joshi B, Johnson R (2012) Touching from a distance: website fingerprinting attacks and defenses. In Proceedings of the 2012 ACM conference on computer and communications security, CCS \u201912, pp 605\u2013616, New York, NY, USA, ACM","DOI":"10.1145\/2382196.2382260"},{"key":"7949_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108472","volume":"199","author":"J Cheng","year":"2021","unstructured":"Cheng J, Yulei W, Yuepeng E, You J, Li T, Li H, Ge J (2021) Matec: a lightweight neural network for online encrypted traffic classification. Comput Netw 199:108472","journal-title":"Comput Netw"},{"key":"7949_CR15","doi-asserted-by":"crossref","unstructured":"Deri L, Martinelli M, Bujlow T, Cardigliano A (2014) ndpi: open-source high-speed deep packet inspection. In 2014 international wireless communications and mobile computing conference (IWCMC), pp 617\u2013622. IEEE","DOI":"10.1109\/IWCMC.2014.6906427"},{"key":"7949_CR16","doi-asserted-by":"crossref","unstructured":"Dierks T, Rescorla E (2008) The transport layer security (tls) protocol version 1.2","DOI":"10.17487\/rfc5246"},{"key":"7949_CR17","doi-asserted-by":"crossref","unstructured":"Draper-Gil G, Lashkari A\u00a0H, Saiful\u00a0Islam MM, Ghorbani Ali\u00a0A (2016) Characterization of encrypted and vpn traffic using time-related. In Proceedings of the 2nd international conference on information systems security and privacy (ICISSP), pp 407\u2013414","DOI":"10.5220\/0005740704070414"},{"key":"7949_CR18","doi-asserted-by":"crossref","unstructured":"Erman J, Mahanti A, Arlitt M, Williamson C (2007) Identifying and discriminating between web and peer-to-peer traffic in the network core. In Proceedings of the 16th international conference on World Wide Web, pp 883\u2013892. ACM","DOI":"10.1145\/1242572.1242692"},{"key":"7949_CR19","doi-asserted-by":"crossref","unstructured":"Frolov S, Wustrow E (2019) The use of tls in censorship circumvention. In NDSS,","DOI":"10.14722\/ndss.2019.23511"},{"key":"7949_CR20","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press, Cambridge"},{"key":"7949_CR21","doi-asserted-by":"crossref","unstructured":"Graves A (2012) Long short-term memory. In Supervised sequence labelling with recurrent neural networks, pp 37\u201345. Springer","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"7949_CR22","doi-asserted-by":"crossref","unstructured":"Graves A (2012) Supervised sequence labelling. In Supervised sequence labelling with recurrent neural networks, pp 5\u201313. Springer","DOI":"10.1007\/978-3-642-24797-2_2"},{"key":"7949_CR23","doi-asserted-by":"crossref","unstructured":"Holz R, Amann J, Mehani O, Wachs M, Ali\u00a0Kaafar M (2016) Tls in the wild: an internet-wide analysis of tls-based protocols for electronic communication. Proceedings 2016 network and distributed system security symposium","DOI":"10.14722\/ndss.2016.23055"},{"key":"7949_CR24","doi-asserted-by":"crossref","unstructured":"Hus\u00e1k M, Cerm\u00e1k M, Jirs\u00edk T, Celeda P (2015) Network-based https client identification using ssl\/tls fingerprinting. In Proceedings of the 2015 10th international conference on availability, reliability and security, ARES \u201915, pp 389\u2013396, USA, IEEE Computer Society","DOI":"10.1109\/ARES.2015.35"},{"key":"7949_CR25","doi-asserted-by":"crossref","unstructured":"Hus\u00e1k M, \u010cerm\u00e1k M, Jirs\u00edk T, \u010celeda P (2016) Https traffic analysis and client identification using passive ssl\/tls fingerprinting. EURASIP J Inf Secur 2016(1)","DOI":"10.1186\/s13635-016-0030-7"},{"key":"7949_CR26","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp 448\u2013456,"},{"key":"7949_CR27","doi-asserted-by":"crossref","unstructured":"Jiang M, Gou G, Shi J, Xiong G (2019) I know what you are doing with remote desktop. In 2019 IEEE 38th international performance computing and communications conference (IPCCC), pp 1\u20137. IEEE","DOI":"10.1109\/IPCCC47392.2019.8958721"},{"key":"7949_CR28","doi-asserted-by":"crossref","unstructured":"Karagiannis T, Papagiannaki K, Faloutsos M (2005) Blinc: multilevel traffic classification in the dark. In ACM SIGCOMM computer communication review, vol\u00a035, pp 229\u2013240. ACM","DOI":"10.1145\/1090191.1080119"},{"key":"7949_CR29","unstructured":"Kazemitabar J, Amini A, Bloniarz A, Talwalkar Ameet\u00a0S (2017) Variable importance using decision trees. Adv Neural Inform Process Syst 30"},{"key":"7949_CR30","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"key":"7949_CR31","doi-asserted-by":"crossref","unstructured":"Kim H, Claffy KC, Fomenkov M, Barman D, Faloutsos M, Lee K (2008) Internet traffic classification demystified: myths, caveats, and the best practices. In Proceedings of the 2008 ACM CoNEXT conference, p\u00a011. ACM","DOI":"10.1145\/1544012.1544023"},{"key":"7949_CR32","doi-asserted-by":"crossref","unstructured":"Kotzias P, Razaghpanah A, Amann J, Paterson Kenneth\u00a0G, Vallina-Rodriguez N, Caballero J (2018) Coming of age: a longitudinal study of tls deployment. In Proceedings of the internet measurement conference 2018, IMC \u201918, pp 415\u2013428, New York, NY, USA, Association for Computing Machinery","DOI":"10.1145\/3278532.3278568"},{"key":"7949_CR33","unstructured":"Kou Y, Lu C-T, Sirwongwattana S, Huang Y-P (2004) Survey of fraud detection techniques. In IEEE international conference on networking, sensing and control, 2004, vol\u00a02, pp 749\u2013754"},{"issue":"2","key":"7949_CR34","first-page":"246","volume":"15","author":"J Leo Breiman","year":"1984","unstructured":"Leo Breiman J, Friedman CJS, Olshen RA (1984) Classification algorithms and regression trees. Classif Regr Trees 15(2):246","journal-title":"Classif Regr Trees"},{"key":"7949_CR35","doi-asserted-by":"publisher","first-page":"18042","DOI":"10.1109\/ACCESS.2017.2747560","volume":"5","author":"M Lopez-Martin","year":"2017","unstructured":"Lopez-Martin M, Carro B, Sanchez-Esguevillas A, Lloret J (2017) Network traffic classifier with convolutional and recurrent neural networks for internet of things. IEEE Access 5:18042\u201318050","journal-title":"IEEE Access"},{"key":"7949_CR36","doi-asserted-by":"crossref","unstructured":"Lotfollahi M, Jafari\u00a0Siavoshani M, Shirali\u00a0Hossein\u00a0Zade R, Saberian Mohammd\u00a0S (2020) Deep packet: a novel approach for encrypted traffic classification using deep learning. Soft Comput","DOI":"10.1007\/s00500-019-04030-2"},{"key":"7949_CR37","first-page":"34","volume":"21","author":"B M\u00f6ller","year":"2014","unstructured":"M\u00f6ller B, Duong T, Kotowicz K (2014) This poodle bites: exploiting the ssl 3.0 fallback. Secur Adv 21:34\u201358","journal-title":"Secur Adv"},{"issue":"3","key":"7949_CR38","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1109\/TNSE.2019.2901994","volume":"7","author":"A Montieri","year":"2019","unstructured":"Montieri A, Ciuonzo D, Bovenzi G, Persico V, Pescap\u00e9 A (2019) A dive into the dark web: hierarchical traffic classification of anonymity tools. IEEE Trans Netw Sci Eng 7(3):1043\u20131054","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"7949_CR39","unstructured":"namespace(7) - linux manual page. https:\/\/man7.org\/linux\/man-pages\/man7\/namespaces.7.html, (2020)"},{"key":"7949_CR40","doi-asserted-by":"crossref","unstructured":"Naylor D, Finamore A, Leontiadis I, Grunenberger Y, Mellia M, Munafo M, Papagiannaki K, Steenkiste P (2014) The cost of the \u2018s\u2019 in https. In Proceedings of the 10th ACM international conference on emerging networking experiments and technologies, pp 133\u2013140,","DOI":"10.1145\/2674005.2674991"},{"key":"7949_CR41","doi-asserted-by":"crossref","unstructured":"Nikiforakis N, Kapravelos A, Joosen W, Kruegel C, Piessens F, Vigna G (2013) Cookieless monster: exploring the ecosystem of web-based device fingerprinting. pp 541\u2013555","DOI":"10.1109\/SP.2013.43"},{"issue":"2","key":"7949_CR42","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1109\/COMST.2018.2883147","volume":"21","author":"F Pacheco","year":"2018","unstructured":"Pacheco F, Exposito E, Gineste M, Baudoin C, Aguilar J (2018) Towards the deployment of machine learning solutions in network traffic classification: a systematic survey. IEEE Commun Surv Tutor 21(2):1988\u20132014","journal-title":"IEEE Commun Surv Tutor"},{"key":"7949_CR43","doi-asserted-by":"crossref","unstructured":"Panchenko A, Lanze F, Pennekamp J, Engel T, Zinnen A, Henze M, Wehrle K (2016) Website fingerprinting at internet scale. In NDSS","DOI":"10.14722\/ndss.2016.23477"},{"issue":"11","key":"7949_CR44","first-page":"2052","volume":"24","author":"H Qinghua","year":"2011","unstructured":"Qinghua H, Che X, Zhang L, Zhang D, Guo M, Daren Yu (2011) Rank entropy-based decision trees for monotonic classification. IEEE Trans Knowl Data Eng 24(11):2052\u20132064","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7949_CR45","doi-asserted-by":"crossref","unstructured":"Razaghpanah A, Akhavan\u00a0Niaki A, Vallina-Rodriguez N, Sundaresan S, Amann J, Gill P (2017) Studying tls usage in android apps. pp 350\u2013362","DOI":"10.1145\/3232755.3232779"},{"issue":"5","key":"7949_CR46","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MCOM.2019.1800819","volume":"57","author":"S Rezaei","year":"2019","unstructured":"Rezaei S, Liu X (2019) Deep learning for encrypted traffic classification: an overview. IEEE Commun Mag 57(5):76\u201381","journal-title":"IEEE Commun Mag"},{"key":"7949_CR47","doi-asserted-by":"crossref","unstructured":"Rimmer V, Preuveneers D, Ju\u00e1rez M, van Goethem T, Joosen W (2017) Automated feature extraction for website fingerprinting through deep learning. CoRR, arXiv: 1708.06376,","DOI":"10.14722\/ndss.2018.23105"},{"issue":"3","key":"7949_CR48","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1109\/COMST.2021.3086296","volume":"23","author":"E Rodriguez","year":"2021","unstructured":"Rodriguez E, Otero B, Gutierrez N, Canal R (2021) A survey of deep learning techniques for cybersecurity in mobile networks. IEEE Commun Surv Tutor 23(3):1920\u20131955","journal-title":"IEEE Commun Surv Tutor"},{"issue":"1","key":"7949_CR49","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"J Ross Quinlan","year":"1986","unstructured":"Ross Quinlan J (1986) Induction of decision trees. Mach Learn 1(1):81\u2013106","journal-title":"Mach Learn"},{"key":"7949_CR50","doi-asserted-by":"crossref","unstructured":"Roughan M, Subhabrata S, Spatscheck O, Duffield N (2004) Class-of-service mapping for QOS: a statistical signature-based approach to ip traffic classification. In Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, pp 135\u2013148. ACM","DOI":"10.1145\/1028788.1028805"},{"key":"7949_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/21-SS133","volume":"16","author":"C Rudin","year":"2022","unstructured":"Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C (2022) Interpretable machine learning: fundamental principles and 10 grand challenges. Statist Surv 16:1\u201385","journal-title":"Statist Surv"},{"key":"7949_CR52","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"7949_CR53","unstructured":"Scrapy (2020) A fast high-level web crawling & scraping framework for python. https:\/\/github.com\/scrapy\/scrapy,"},{"key":"7949_CR54","doi-asserted-by":"crossref","unstructured":"Shen M, Liu Y, Chen S, Zhu L, Zhang Y (2019) Webpage fingerprinting using only packet length information. In ICC 2019-2019 IEEE international conference on communications (ICC), pp 1\u20136. IEEE","DOI":"10.1109\/ICC.2019.8761167"},{"key":"7949_CR55","doi-asserted-by":"crossref","unstructured":"Sherry J, Lan C, Popa R\u00a0A, Ratnasamy S (2015) Blindbox: deep packet inspection over encrypted traffic. In ACM SIGCOMM computer communication review, vol\u00a045, pp 213\u2013226. ACM","DOI":"10.1145\/2829988.2787502"},{"key":"7949_CR56","doi-asserted-by":"crossref","unstructured":"Sirinam P, Imani M, Ju\u00e1rez M, Wright M (2018) Deep fingerprinting: undermining website fingerprinting defenses with deep learning. CoRR, arXiv:1801.02265,","DOI":"10.1145\/3243734.3243768"},{"key":"7949_CR57","doi-asserted-by":"crossref","unstructured":"Soltani M, Ousat B, Jafari\u00a0Siavoshani M, Jahangir AH (2021) An adaptable deep learning-based intrusion detection system to zero-day attacks. CoRR, arXiv:2108.09199,","DOI":"10.1007\/s10207-021-00567-2"},{"key":"7949_CR58","doi-asserted-by":"crossref","unstructured":"Soltani M, Siavoshani MJ, Jahangir AH (2021) A content-based deep intrusion detection system. Int J Inform Secur 1\u201316","DOI":"10.1007\/s10207-021-00567-2"},{"key":"7949_CR59","volume-title":"Computer security: principles and practice","author":"W Stallings","year":"2014","unstructured":"Stallings W, Brown L (2014) Computer security: principles and practice, 3rd edn. Prentice Hall Press, Upper Saddle River, NJ, USA","edition":"3"},{"key":"7949_CR60","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"H Trevor","year":"2009","unstructured":"Trevor H, Robert T, Jerome F (2009) The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, Heidelberg"},{"key":"7949_CR61","doi-asserted-by":"crossref","unstructured":"van Ede T, Bortolameotti R, Continella A, Ren J, Dubois DJ, Lindorfer M, Choffnes D, van Steen M, Peter A (2020) Flowprint: semi-supervised mobile-app fingerprinting on encrypted network traffic. In Network and distributed system security symposium (NDSS), vol\u00a027,","DOI":"10.14722\/ndss.2020.24412"},{"key":"7949_CR62","unstructured":"Vera R, Davy P, Marc J, Tom\u00a0VG, Wouter J (2018) Automated website fingerprinting through deep learning. In Proceedings 2018 network and distributed system security symposium"},{"key":"7949_CR63","doi-asserted-by":"crossref","unstructured":"Verleysen M, Fran\u00e7ois D (2005) The curse of dimensionality in data mining and time series prediction. In International work-conference on artificial neural networks, pp 758\u2013770. Springer","DOI":"10.1007\/11494669_93"},{"key":"7949_CR64","doi-asserted-by":"crossref","unstructured":"Wang W, Zhu M, Wang J, Zeng X, Yang Z (2017) End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In Intelligence and Security Informatics (ISI), 2017 IEEE International Conference on, pp 43\u201348. IEEE","DOI":"10.1109\/ISI.2017.8004872"},{"key":"7949_CR65","unstructured":"Wang W, Zhu M, Zeng X, Ye X, Sheng Y (2017) Malware traffic classification using convolutional neural network for representation learning. In 2017 International Conference on Information Networking (ICOIN), pp 712\u2013717. IEEE"},{"key":"7949_CR66","doi-asserted-by":"publisher","first-page":"2166","DOI":"10.1109\/TIFS.2022.3179955","volume":"17","author":"SJ Xu","year":"2022","unstructured":"Xu SJ, Geng G-G, Jin X-B, Liu D-J, Weng J (2022) Seeing traffic paths: encrypted traffic classification with path signature features. IEEE Trans Inform Forens Secur 17:2166\u20132181","journal-title":"IEEE Trans Inform Forens Secur"},{"key":"7949_CR67","first-page":"66","volume":"62","author":"T-F Yen","year":"2012","unstructured":"Yen T-F, Xie Y, Fang Yu, Roger Peng Yu, Abadi M (2012) Host fingerprinting and tracking on the web: privacy and security implications. NDSS 62:66","journal-title":"NDSS"},{"issue":"5","key":"7949_CR68","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1016\/j.jnca.2009.03.001","volume":"32","author":"Lin Ying-Dar","year":"2009","unstructured":"Ying-Dar Lin, Chun-Nan Lu, Yuan-Cheng Lai, Wei-Hao Peng, Po-Ching Lin (2009) Application classification using packet size distribution and port association. J Netw Comput Appl 32(5):1023\u20131030","journal-title":"J Netw Comput Appl"},{"key":"7949_CR69","doi-asserted-by":"crossref","unstructured":"Zliobaite I, Pechenizkiy M, Gama J (2016) An overview of concept drift applications. In Big data analysis: new algorithms for a new society, pp 91\u2013114. Springer","DOI":"10.1007\/978-3-319-26989-4_4"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-07949-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-07949-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-07949-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T17:55:23Z","timestamp":1683827723000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-07949-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,28]]},"references-count":69,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["7949"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-07949-9","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2023,3,28]]},"assertion":[{"value":"20 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Mahdi Jafari Siavoshani, Amirhossein Khajehpour, Amirmohammad Ziaei, Amirali Gatmiri, and Ali Taheri declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}