出版社：University of Malaya * Faculty of Computer Science and Information Technology
摘要：This paper provides a comprehensive review and analysis of the detection of suspicious terrorist electronic mails (emails) using various phases and methods of text classification. We explored, analyzed, and compared different datasets, features, feature extraction techniques, feature representation techniques, feature selection schemes, text classification techniques, and performance measurement metrics used in the detection of suspicious terrorist emails. 30 articles were retrieved from 6 wellknown academic databases after rigorous selection. From the study, we found that researchers often generate their own emails dataset since there is no public dataset is available in the research area of detecting suspicious terrorist emails. In most of the studies, researchers used content and contextbased features to detect terrorist emails. Our findings also show that the most commonly used feature extraction techniques are the bag of words and ngram, the most typically applied feature representation schemes are binary representation and term frequency, the most usually adopted feature selection method is information gain,, the most common and most accurate text classification algorithms are naïve bayes, decision trees, and support vector machines, and the widely employed performance measurement metrics are accuracy, precision, and recall. Open research challenges and research issues that involve significant research efforts are also summarized in this review for future researchers in the area of suspicious terrorist email detection using text classification techniques where the critical analysis presented in this paper also provides valuable insights to guide these researchers. Finally, the indicated issues and challenges presented in this paper can be used as future research directions in this area.
