An Efficient BotNet Detection Scheme Exploiting Word2Vec and Accelerated Hierarchical Density-based Clustering |
Lee, Taeil
(Dept. of Computer Science and Information Engineering, Korea National University of Transportation)
Kim, Kwanhyun (Dept. of Computer Science and Information Engineering, Korea National University of Transportation) Lee, Jihyun (Dept. of Computer Science and Information Engineering, Korea National University of Transportation) Lee, Suchul (Dept. of Computer Science and Information Engineering, Korea National University of Transportation) |
1 | H. G. Kim et al. "Visualization of Malwares for Classification Through Deep Learning," Journal of Internet Computing and Services(JICS), 19(5), pp. 67-75, Oct. 2018. http://dx.doi.org/10.7472/jksii.2018.19.5.67 DOI |
2 | E. Hodo et al, "Shallow and deep networks intrusion detection system: A taxonomy and survey", arXiv preprint arXiv:1701.02145 https://arxiv.org/abs/1701.02145 |
3 | S. Ryu et al. A Comparative Study of Machine Learning Algorithms and Their Ensembles for Botnet Detection. Journal of Computer and Communications, 6(5), 119-129, 2018. https://dx.doi.org/10.4236/jcc.2018.65010 DOI |
4 | Vasiliadis et al, "MIDeA: a multi-parallel intrusion detection architecture," In ACM conference on Computer and communications security (CCS) 2011. https://dl.acm.org/citation.cfm?id=2046741 |
5 | Song Yangqiu et al, "Unsupervised sparse vector densification for short text similarity," Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015. https://aclweb.org/anthology/N15-1138 |
6 | M. Tomas et al. "Distributed representations of words and phrases and their compositionality." Advances in Neural Information Processing Systems (NIPS) 2013. https://dl.acm.org/citation.cfm?id=2999959 |
7 | R. S. M. Carrasco et al, "Unsupervised intrusion detection through skip-gram models of network behavior." Computers & Security 78 (2018): 187-197. https://doi.org/10.1016/j.cose.2018.07.003 DOI |
8 | Popov, I. "Malware detection using machine learning based on Word2Vec embeddings of machine code instructions" Siberian Symposium on Data Science and Engineering 2017. https://ieeexplore.ieee.org/document/8071952 |
9 | S. Garcia, M. Grill, "An empirical comparison of botnet detection methods," Computers & Security, vol. 45, pp. 100-123, 2014. https://doi.org/10.1016/j.cose.2014.05.011 DOI |
10 | S. Lee et al., "NeTraMark: a network traffic classification benchmark," ACM SIGCOMM Computer Communication Review 41.1, 22-30, 2011. http://doi.acm.org/10.1145/1925861.1925865 DOI |
11 | K. C. Claffy et al, "A parameterizable methodology for Internet traffic flow profiling." IEEE Journal on selected areas in communications, 13.8, 1481-1494, 1995. https://doi.org/10.1109/49.464717 DOI |
12 | P. Sethi et al, "Internet of things: architectures, protocols, and applications," Journal of Electrical and Computer Engineering, 2017. https://doi.org/10.1155/2017/9324035 |
13 | L. Yang et al. "Topical word embeddings," Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.703.7444&rep=rep1&type=pdf |
14 | X. Rong, "Word2Vec parameter learning explained," arXiv:1411.2738, 2014. https://arxiv.org/abs/1411.2738 |
15 | Mnih, Andriy, and Koray Kavukcuoglu. "Learning word embeddings efficiently with noise-contrastive estimation." Advances in Neural Information Processing Systems, 2013. (NIPS 2013) http://papers.nips.cc/paper/5165-learning-word-embeddings-efficiently-with |
16 | S. Frank et al. "Feature engineering in context-dependent deep neural networks for conversational speech transcription," 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011. https://doi.org/10.1109/ASRU.2011.6163899 |
17 | A. Kilgarriff. "Thesauruses for natural language processing." International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings, IEEE, 2003. https://doi.org/10.1109/NLPKE.2003.1275859 |
18 | L. Maaten et al, "Visualizing data using t-SNE." Journal of machine learning research, 9, 2579-2605, Nov. 2008. http://www.jmlr.org/papers/v9/vandermaaten08a.html |
19 | L. Maaten et al, "Visualizing data using t-SNE." Journal of machine learning research 9.Nov (2008): 2579-2605. http://www.jmlr.org/papers/v9/vandermaaten08a.html |
20 | E. Martin et al. "A density-based algorithm for discovering clusters in large spatial databases with noise," Kdd. Vol. 96. No. 34. 1996. https://dl.acm.org/citation.cfm?id=3001507 |
21 | S. Lee et al, "LARGen: Automatic Signature Generation for Malwares Using Latent Dirichlet Allocation," IEEE Transactions on Dependable and Secure Computing (TDSC) Vol.15(5), pp. 771-783, 2018. https://doi.org/10.1109/TDSC.2016.2609907 DOI |
22 | G. Salton et al, "A Vector space model for automatic indexing." Communications of the ACM, Vol.18(11), pp. 613-620, 1975. https://doi.org/10.1145/361219.361220 DOI |
23 | D. Scott, et al. "Indexing by latent semantic analysis," Journal of the American society for information science 41.6, 391-407, 1990. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DOI |
24 | H. Thomas. "Probabilistic latent semantic analysis," Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 1999. https://dl.acm.org/citation.cfm?id=2073829 |
25 | T. N. Rubin et al, "Statistical topic models for multi-label document classification," Machine Learning, Vol.88 (1-2), pp. 157-208, 2012. https://doi.org/10.1007/s10994-011-5272-5 DOI |