Email security level classification of imbalanced data using artificial neural network: The real case in a world-leading enterprise. (October 2018)

• Record Type: Journal Article
• Authors: Huang, Jen-Wei; Chiang, Chia-Wen; Chang, Jia-Wei
• Is Part Of: Engineering applications of artificial intelligence. Volume 75(2018)
• Page Start: 11
• Page End: 21
• Publication Date: 2018-10
• Subjects: E-mail -- Classifier -- Text mining -- Artificial neural network; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.07.010
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Email is far more convenient than traditional mail in the delivery of messages. However, it is susceptible to information leakage in business. This problem can be alleviated by classifying emails into different security levels using text mining and machine learning technology. In this research, we developed a scheme in which a neural network is used to extract information from emails to enable its transformation into a multidimensional vector. Email text data is processed using bi-gram to train the document vector, which then undergoes under-sampling to deal with the problem of data imbalance. Finally, the security label of emails is classified using an artificial neural network. The proposed system was evaluated in an actual corporate setting. The results show that the proposed feature extraction approach is more effective than existing methods for the representations of email data in true positive rates and F1-scores. Highlights: An effective and efficient model to classify the Email Security Level. Address the data imbalance problem which is common in a real-world application. The experimental dataset that collected from a world-leading enterprise. The experiments compared the well-known approaches of the semantic representation. The proposed system is now utilized in a world-leading enterprise.