Deep Quarantine for Suspicious Mail

Nikita Benkovich, Roman Dedenok and Dmitry Golubev

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper, we introduce DeepQuarantine (DQ), a cloudtechnology to detect and quarantine potential spam messages. Spam at-tacks are becoming more diverse and can potentially be harmful to emailusers. Despite the high quality and performance of spam filtering sys-tems, detection of a spam campaign can take some time. Unfortunately,in this case some unwanted messages get delivered to users. To solve thisproblem, we created DQ, which detects potential spam and keeps it ina special Quarantine folder for a while. The time gained allows us todouble-check the messages to improve the reliability of the anti-spam so-lution. Due to high precision of the technology, most of the quarantinedmail is spam, which allows clients to use email without delay. Our solutionis based on applying Convolutional Neural Networks on MIME headersto extract deep features from large-scale historical data. We evaluatedthe proposed method on real-world data and showed that DQ enhancesthe quality of spam detection.

Keywords: spam filtering; spam detection; machine learning; deeplearning; cloud technology (search for similar items in EconPapers)
JEL-codes: C45 M15 (search for similar items in EconPapers)
Date: 2019-09-23, Revised 2019-09-23
New Economics Papers: this item is included in nep-cmp, nep-ore and nep-pay
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