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Loshin, \u201cDetails emerging on Dyn DNS DDoS attack, Mirai IoT botnet,\u201d TechTarget network, http:\/\/searchsecurity.techtarget.com\/news\/450401962\/Details-emerging-on-Dyn-DNS-DDoS-attack-Mirai-IoT-botnet, accessed Jan. 18. 2019."},{"key":"2","unstructured":"[2] M. Kuzin, Y. Shmelev, and V. Kuskov, \u201cNew trends in the world of IoT threats,\u201d AO Kaspersky Lab, https:\/\/securelist.com\/new-trends-in-the-world-of-iot-threats\/87991\/, accessed Feb. 6. 2019."},{"key":"3","unstructured":"[3] L.J. Rivera and L. Goasduff, \u201cGartner says a thirty-fold increase in internet-connected physical devices by 2020 will significantly alter how the supply chain operates,\u201d Gartner, https:\/\/www.gartner.com\/newsroom\/id\/2688717, accessed Jan. 18. 2019."},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] Y.M.P. Pa, S. Suzuki, K. Yoshioka, T. Matsumoto, T. Kasama, and C. Rossow, \u201cIoTPOT: A novel honeypot for revealing current IoT threats,\u201d J. Information Processing, vol.24, no.3, pp.522-533, 2016. 10.2197\/ipsjjip.24.522","DOI":"10.2197\/ipsjjip.24.522"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] T.F. Yen, V. Heorhiadi, A. Oprea, M.K. Reiter, and A. Juels, \u201cAn epidemiological study of malware encounters in a large enterprise,\u201d Proc. 2014 ACM SIGSAC Conference on Computer and Communications Security, ACM, pp.1117-1130, 2014. 10.1145\/2660267.2660330","DOI":"10.1145\/2660267.2660330"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] M.M. Masud, L. Khan, and B. Thuraisingham, \u201cA scalable multi-level feature extraction technique to detect malicious executables,\u201d Inform. Syst. Front., vol.10, no.1, pp.33-45, 2008. 10.1007\/s10796-007-9054-3","DOI":"10.1007\/s10796-007-9054-3"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] M. Ahmadi, D. Ulyanov, S. Semenov, M. Trofimov, and G. Giacinto, \u201cNovel feature extraction, selection and fusion for effective malware category classification,\u201d Proc. Sixth ACM Conference on Data and Application Security and Privacy, ACM, pp.183-194, 2016. 10.1145\/2857705.2857713","DOI":"10.1145\/2857705.2857713"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J. Drew, T. Moore, and M. Hahsler, \u201cPolymorphic malware detection using sequence classification methods,\u201d Proc. 2016 IEEE Security and Privacy Workshops (SPW), 2016. 10.1109\/spw.2016.30","DOI":"10.1109\/SPW.2016.30"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] F. Shahzad and M. Farooq, \u201cELF-Miner: Using structural knowledge and data mining methods to detect new (Linux) malicious executables,\u201d Knowledge and Information Systems, vol.30, no.3, pp.589-612, 2012. 10.1007\/s10115-011-0393-5","DOI":"10.1007\/s10115-011-0393-5"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] J. Bai, Y. Yang, S. Mu, and Y. Ma, \u201cMalware detection through mining symbol table of Linux executables,\u201d Information Technology J., vol.12, no.2, pp.380-384, 2013. 10.3923\/itj.2013.380.384","DOI":"10.3923\/itj.2013.380.384"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] X. Wang, W. Yu, A. Champion, X. Fu, and D. Xuan, \u201cDetecting worms via mining dynamic program execution,\u201d Proc. 2007 Third International Conference on Security and Privacy in Communications Networks and the Workshops-SecureComm 2007, pp.412-421, 2007. 10.1109\/seccom.2007.4550362","DOI":"10.1109\/SECCOM.2007.4550362"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] H.-S. Ham, H.-H. Kim, M.-S. Kim, and M.-J. Choi, \u201cLinear SVM-based android malware detection for reliable IoT services,\u201d J. Applied Mathematics, vol.2014, 2014. 10.1155\/2014\/594501","DOI":"10.1155\/2014\/594501"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] A. Azmoodeh, A. Dehghantanha, and K.-K.R. Choo, \u201cRobust malware detection for internet of (battlefield) things devices using deep eigenspace learning,\u201d IEEE Trans. Sustain. Comput., vol.4, no.1, pp.88-95, 2019. 10.1109\/tsusc.2018.2809665","DOI":"10.1109\/TSUSC.2018.2809665"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] J. Su, D.V. Vargas, S. Prasad, D. Sgandurra, Y. Feng, and K. Sakurai, \u201cLightweight classification of IoT malware based on image recognition,\u201d Proc. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan, 2018. 10.1109\/compsac.2018.10315","DOI":"10.1109\/COMPSAC.2018.10315"},{"key":"15","unstructured":"[15] B. Krebs, \u201cSource code for IoT botnet \u2018Mirai\u2019 released,\u201d https:\/\/krebsonsecurity.com\/2016\/10\/source-code-for-iot-botnet-Mirai-released\/, accessed Jan. 18. 2019."},{"key":"16","unstructured":"[16] J. Gamblin, \u201cMirai-Source-Code,\u201d GitHub, https:\/\/github.com\/jgamblin\/Mirai-Source-Code\/, accessed Jan. 18. 2019."},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] W.H. Gomaa and A.A. Fahmy, \u201cA survey of text similarity approaches,\u201d International Journal of Computer Applications, vol.68, no.13, pp.13-18, 2013. 10.5120\/11638-7118","DOI":"10.5120\/11638-7118"},{"key":"18","unstructured":"[18] A. Tellez, \u201cBashlite,\u201d GitHub, https:\/\/github.com\/anthonygtellez\/BASHLITE, accessed Jan. 18. 2019."},{"key":"19","unstructured":"[19] D. Davidson, \u201clinux.mirai,\u201d https:\/\/github.com\/0x27\/, accessed Jan. 18. 2019."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] G. Kondrak, \u201cN-gram similarity and distance,\u201d International Symposium on String Processing and Information Retrieval, pp.115-126, Springer Berlin Heidelberg, 2005. 10.1007\/11575832_13","DOI":"10.1007\/11575832_13"},{"key":"21","unstructured":"[21] P.F. Brown, P.V. Desouza, R.L. Mercer, V.J.D. Pietra, and J.C. Lai, \u201cClass-based n-gram models of natural language,\u201d Computational Linguistics, vol.18, no.4, pp.467-479, 1992."},{"key":"22","unstructured":"[22] I.H. Witten, E. Frank, and M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2016."},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] M. Bailey, J. Oberheide, J. Andersen, Z.M. Mao, F. Jahanian, and J. Nazario, \u201cAutomated classification and analysis of internet malware,\u201d Recent Advances in Intrusion Detection, C. Kruegel, L. Lippmann, and C Andrew, eds., Lecture Notes in Computer Science, vol.4637, pp.178-197, 2007. 10.1007\/978-3-540-74320-0_10","DOI":"10.1007\/978-3-540-74320-0_10"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] T. Joachims, \u201cText categorization with support vector machines: Learning with many relevant features,\u201d European Conference on Machine Learning, pp.137-142, 1998. 10.1007\/bfb0026683","DOI":"10.1007\/BFb0026683"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] A. Hotho, A. N\u00fcrnberger, and G. Paa\u00df, \u201cA brief survey of text mining,\u201d LDV Forum, vol.20, no.1, pp.19-62, 2005.","DOI":"10.21248\/jlcl.20.2005.68"},{"key":"26","unstructured":"[26] K.P. Murphy, Naive Bayes Classifiers, University of British Columbia, Ph.D. Thesis, 2006."},{"key":"27","unstructured":"[27] I. Rish, \u201cAn empirical study of the naive Bayes classifier,\u201d IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol.3, no.22, pp.41-46, 2001."},{"key":"28","unstructured":"[28] K. Sato, \u201cAn inside look at google bigquery,\u201d White paper, Google, https:\/\/cloud.google.com\/files\/BigQueryTechnicalWP.pdf, accessed Jan. 18. 2019."},{"key":"29","unstructured":"[29] F. Pedregosa et al., \u201cScikit-learn: Machine learning in Python,\u201d J. Machine Learning Research, vol.12, pp.2825-2830, Oct. 2011."},{"key":"30","unstructured":"[30] die.net, \u201cptmx(4)-Linux man page,\u201d https:\/\/linux.die.net\/man\/4\/ptmx, accessed Jan. 18. 2019."},{"key":"31","unstructured":"[31] J. Trost, \u201c7up (Mirai?) Triage, More IoT Malware Targeting Weak Passwords,\u201d http:\/\/www.covert.io\/7up-mirai-triage-more-iot-malware-targeting-weak-passwords\/, accessed Jan. 18. 2019."},{"key":"32","unstructured":"[32] C. Zheng, C. Xiao, and Y. Jia, \u201cIoT malware evolves to harvest bots by exploiting a zero-day home router vulnerability,\u201d Palo Alto Networks, https:\/\/researchcenter.paloaltonetworks.com\/2018\/01\/unit42-iot-malware-evolves-harvest-bots-exploiting-zero-day-home-router-vulnerability\/, accessed Jan. 18. 2019."}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E103.B\/1\/E103.B_2019CPP0009\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T14:57:43Z","timestamp":1704898663000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E103.B\/1\/E103.B_2019CPP0009\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,1]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/transcom.2019cpp0009","relation":{},"ISSN":["0916-8516","1745-1345"],"issn-type":[{"value":"0916-8516","type":"print"},{"value":"1745-1345","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,1]]}}}