A semantic-based classification approach for an enhanced spam detection. Issue 94 (July 2020)

• Record Type: Journal Article
• Authors: Saidani, Nadjate; Adi, Kamel; Allili, Mohand Saïd
• Is Part Of: Computers & security. Issue 94(2020)
• Publication Date: 2020-07
• Subjects: Spam detection -- Domain-specific analysis -- Semantic features -- Multilevel analysis -- Classification; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2020.101716
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results.