Abstract
Data leakage is a potentially important issue for businesses. Numerous corporate offer data loss prevention (DLP) solutions to monitor information flow, and detect such leakage. Adding a secret label to a document, DLP can use documents label to do securely control, effectively protecting data. With the increasing documents every day, manual labeling is time-consuming. To better solve the difficult task, recently researchers need to start use document security identification by machine learning quickly classify a large number of texts. The contribution of this paper is to explore dimensionality reduction by feature selection and combine two models to avoid the process of weighting different type of features. In contrast to training all features with one algorithm, our experimental results demonstrate that the combination of two models can improve the classification performance.
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References
Alzhrani, K., Rudd, E.M., Boult, T.E., et al.: Automated big text security classification (2016)
Engelstad, P.E., Hammer, H., Kongsgard, K.W., et al.: Automatic security classification with lasso. In: International Workshop on Information Security Applications, pp. 399–410. Springer International Publishing (2015)
Kongsgard, K.W., Nordbotten, N.A., Mancini, F., et al.: Data loss prevention based on text classification in controlled environments. In: Information Systems Security, pp. 131–150. Springer, Berlin (2016)
Alneyadi, S., Sithirasenan, E., Muthukkumarasamy, V.: Word N-gram based classification for data leakage prevention. In: 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 578–585. IEEE (2013)
Hammer, H., Kongsgard, K.W., Bai, A., et al.: Automatic security classification by machine learning for cross-domain information exchange. In: Military Communications Conference, Milcom 2015, pp. 1590–1595. IEEE (2015)
Engelstad, P.E., Hammer, H., Yazidi, A., et al.: Advanced classification lists (dirty word lists) for automatic security classification. In: 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 44–53. IEEE (2015)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)
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Gu, K., Li, H., Sun, G. (2018). Document Security Identification Based on Multi-classifier. In: Abawajy, J., Choo, KK., Islam, R. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence. ATCI 2017. Advances in Intelligent Systems and Computing, vol 580. Edizioni della Normale, Cham. https://doi.org/10.1007/978-3-319-67071-3_18
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DOI: https://doi.org/10.1007/978-3-319-67071-3_18
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Publisher Name: Edizioni della Normale, Cham
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Online ISBN: 978-3-319-67071-3
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