Method of forming multi-leveled sequential patterns

A.V. Moldavskaya

Abstract


The research is dedicated to the problem of large volumes of results acquired from sequential pattern mining. The new form of sequential patterns is proposed. The requirements for a programmed implementation of the described method are introduced. The results of experiments based on real malware behavior data are demonstrated.

Problems in programming 2016; 2-3: 158-163


Keywords


data mining; sequential pattern mining; regular expressions

References


Srikant, R. and Agrawal, R. (1996). Mining sequential patterns. San Jose [etc.]: IBM Thomas J. Watson Research Division.

Gupta, M. and Han, J. (n.d.). Approaches for Pattern Discovery Using Sequential Data Mining. Pattern Discovery Using Sequence Data Mining, pp.137-154.

https://doi.org/10.4018/978-1-61350-056-9.ch008

Zhu, F., Yan, X., Han, J. and Yu, P. (2007). Efficient Discovery of Frequent Approximate Sequential Patterns. Seventh IEEE International Conference on Data Mining (ICDM 2007), pp.751 - 756.

https://doi.org/10.1109/ICDM.2007.75

Mabroukeh, N. and Ezeife, C. (2010). A taxonomy of sequential pattern mining algorithms. CSUR, 43(1), pp.1-41. https://doi.org/10.1145/1824795.1824798

Fournier-Viger, P et. al. (2014) SPMF: a Java open-source pattern mining library. J. Mach. Learn. Res. 15, 1, 3389-3393.

Chen, Z., Roussopoulos, M., Liang, Z., Zhang, Y., Chen, Z. and Delis, A. (2012). Malware characteristics and threats on the internet ecosystem. Journal of Systems and Software, 85(7), pp.1650-1672. https://doi.org/10.1016/j.jss.2012.02.015

Sami, A., Yadegari, B., Peiravian, N., Hashemi, S. and Hamze, A. (2010). Malware detection based on mining API calls. Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10, pp.1020-1025. https://doi.org/10.1145/1774088.1774303




DOI: https://doi.org/10.15407/pp2016.02-03.158

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