{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:08:45Z","timestamp":1742947725690,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030786113"},{"type":"electronic","value":"9783030786120"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78612-0_24","type":"book-chapter","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T23:38:40Z","timestamp":1625787520000},"page":"289-304","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An N-gram Based Deep Learning Method for Network Traffic Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3490-963X","authenticated-orcid":false,"given":"Wang","family":"Xiaojuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3107-1659","authenticated-orcid":false,"given":"Kaiwenlv","family":"Kacuila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2896-4595","authenticated-orcid":false,"given":"He","family":"Mingshu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36784-7","volume-title":"Data Traffic Monitoring and Analysis","author":"E Biersack","year":"2013","unstructured":"Biersack, E., Christian, C., Maja, M.: Data Traffic Monitoring and Analysis. Springer, Berlin (2013)"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Dharmapurikar, S., et al.: Deep packet inspection using parallel Bloom filters. IEEE Micro 24(1), 52\u201361 (2004)","DOI":"10.1109\/MM.2004.1268997"},{"issue":"4","key":"24_CR3","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/SURV.2008.080406","volume":"10","author":"TTT Nguyen","year":"2008","unstructured":"Nguyen, T.T.T., Grenville, A.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tutorials 10(4), 56\u201376 (2008)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"24_CR4","unstructured":"Wang, W., et al.: Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking (ICOIN). IEEE (2017)"},{"issue":"2","key":"24_CR5","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1109\/SURV.2013.100613.00161","volume":"16","author":"M Finsterbusch","year":"2013","unstructured":"Finsterbusch, M., et al.: A survey of payload-based traffic classification approaches. IEEE Commun. Surv. Tutorials 16(2), 1135\u20131156 (2013)","journal-title":"IEEE Commun. Surv. Tutorials"},{"issue":"1","key":"24_CR6","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.eswa.2010.06.066","volume":"38","author":"SJ Horng","year":"2011","unstructured":"Horng, S.J., et al.: A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst. Appl. Int. J. 38(1), 306\u2013313 (2011)","journal-title":"Expert Syst. Appl. Int. J."},{"key":"24_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-642-31537-4_46","volume-title":"Machine Learning and Data Mining in Pattern Recognition","author":"I Syarif","year":"2012","unstructured":"Syarif, I., Zaluska, E., Prugel-Bennett, A., Wills, G.: Application of bagging, boosting and stacking to intrusion detection. In: Perner, P. (ed.) MLDM 2012. LNCS (LNAI), vol. 7376, pp. 593\u2013602. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-31537-4_46"},{"key":"24_CR8","unstructured":"Li, Z., et al.: Intrusion detection using convolutional neural networks for representation learning. In: International Conference on Neural Information Processing. Springer, Cham (2014)"},{"key":"24_CR9","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1016\/j.procs.2015.07.490","volume":"57","author":"P Aggarwal","year":"2015","unstructured":"Aggarwal, P., Sharma, S.K.: Analysis of KDD dataset attributes - class wise for intrusion detection. Procedia Comput. Sci. 57, 842\u2013851 (2015)","journal-title":"Procedia Comput. Sci."},{"issue":"99","key":"24_CR10","doi-asserted-by":"publisher","first-page":"1792","DOI":"10.1109\/ACCESS.2017.2780250","volume":"6","author":"W Wang","year":"2018","unstructured":"Wang, W., et al.: HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6(99), 1792\u20131806 (2018)","journal-title":"IEEE Access"},{"key":"24_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/978-3-030-00202-2_23","volume-title":"Modeling Decisions for Artificial Intelligence","author":"J Cui","year":"2018","unstructured":"Cui, J., Long, J., Min, E., Mao, Y.: WEDL-NIDS: improving network intrusion detection using word embedding-based deep learning method. In: Torra, V., Narukawa, Y., Aguil\u00f3, I., Gonz\u00e1lez-Hidalgo, M. (eds.) MDAI 2018. LNCS (LNAI), vol. 11144, pp. 283\u2013295. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00202-2_23"},{"issue":"1","key":"24_CR12","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/MNET.2012.6135854","volume":"26","author":"A Dainotti","year":"2012","unstructured":"Dainotti, A., Pescape, A., Claffy, K.C.: Issues and future directions in traffic classification. IEEE Netw. 26(1), 35\u201340 (2012)","journal-title":"IEEE Netw."},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Koukis, D., et al.: A generic anonymization framework for network traffic. In: 2006 IEEE International Conference on Communications, vol. 5. IEEE (2006)","DOI":"10.1109\/ICC.2006.255113"},{"key":"24_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1007\/11856214_12","volume-title":"Intenational Workshp on Recent Advances in Intrusion Detection","author":"K Wang","year":"2006","unstructured":"Wang, K., Parekh, J.J., Stolfo, S.J.: Anagram: a content anomaly detector resistant to mimicry attack. In: Zamboni, D., Kruegel, C. (eds.) RAID 2006. LNCS, vol. 4219, pp. 226\u2013248. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11856214_12"},{"issue":"9","key":"24_CR15","first-page":"317","volume":"2","author":"I Santos","year":"2009","unstructured":"Santos, I., et al.: N-grams-based file signatures for malware detection. ICEIS 2(9), 317\u2013320 (2009)","journal-title":"ICEIS"},{"key":"24_CR16","unstructured":"Zhao, Z., et al.: Advancing feature selection research. ASU Feature Selection Repository, pp. 1\u201328 (2010)"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Ajay Kumara, M.A., Jaidhar, C.D.: Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor. Digital Invest. 23, 99\u2013123 (2017)","DOI":"10.1016\/j.diin.2017.10.004"},{"key":"24_CR18","first-page":"2721","volume":"7","author":"JZ Kolter","year":"2006","unstructured":"Kolter, J.Z., Maloof, M.A.: Learning to detect and classify malicious executables in the wild. J. Mach. Learn. Res. 7, 2721\u20132744 (2006)","journal-title":"J. Mach. Learn. Res."},{"issue":"04","key":"24_CR19","doi-asserted-by":"publisher","first-page":"95","DOI":"10.4236\/jdaip.2014.24012","volume":"2","author":"B Singh","year":"2014","unstructured":"Singh, B., Kushwaha, N., Vyas, O.P., et al.: A feature subset selection technique for high dimensional data using symmetric uncertainty. J. Data Anal. Inform. Process. 2(04), 95\u2013105 (2014)","journal-title":"J. Data Anal. Inform. Process."},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Coronado-De-Alba, L.D., Rodr\u00b4\u0131guez-Mota, A., Escamilla-Ambrosio, P.J.: Feature selection and ensemble of classifiers for android malware detection. In: Proceedings of the 8th IEEE Latin-American Conference on Communications (LATINCOM), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/LATINCOM.2016.7811605"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: Proceedings of the International Conference on Engineering and Technology (ICET), pp. 1\u20136 (2017)","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"issue":"2","key":"24_CR22","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2016","unstructured":"Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tuts. 18(2), 1153\u20131176 (2016)","journal-title":"IEEE Commun. Surv. Tuts."},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Berman, D., Buczak, A., Chavis, J., Corbett, C.: A survey of deep learning methods for cyber security. Information 10(4), 122 (2019)","DOI":"10.3390\/info10040122"},{"issue":"6","key":"24_CR24","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3390\/e19060242","volume":"19","author":"S Albelwi","year":"2017","unstructured":"Albelwi, S., Mahmood, A.: A framework for designing the architectures of deep convolutional neural networks. Entropy 19(6), 242 (2017)","journal-title":"Entropy"},{"issue":"3","key":"24_CR25","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","volume":"31","author":"A Shiravi","year":"2012","unstructured":"Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31(3), 357\u2013374 (2012)","journal-title":"Comput. Secur."},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP, pp. 108\u2013116 (2018)","DOI":"10.5220\/0006639801080116"},{"issue":"9","key":"24_CR27","doi-asserted-by":"publisher","first-page":"177","DOI":"10.13052\/jsn2445-9739.2017.009","volume":"2017","author":"I Sharafaldin","year":"2017","unstructured":"Sharafaldin, I., et al.: Towards a reliable intrusion detection benchmark dataset. Softw. Netw. 2017(9), 177\u2013200 (2017)","journal-title":"Softw. Netw."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78612-0_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T22:05:55Z","timestamp":1630361155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78612-0_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030786113","9783030786120"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78612-0_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"ICAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"incodldos2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icaisconf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}