Deep learning based spam detection system

Girija Chetty, Hieu Bui, Matthew White

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

In this paper, we propose a deep learning-based spam detection model. This model is a combination of the Word Embedding technique and Neural Network algorithm. Word Embedding allows a distributed representation of words in the feature space where word's meaning and word analogy can be represented. Deep neural network is used to learn features of text documents represented in the embedding space and use these features to classify text documents. This model architecture is expected to be able to effectively detect spams in various types of text documents as well as in large document corpus.

Original languageEnglish
Title of host publicationProceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019
EditorsPhill Kyu Rhee, Kuo-Yuan Hwa, Tun-Wen Pai, Daniel Howard, Rezaul Bashar
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages91-96
Number of pages6
ISBN (Electronic)9781728104041
ISBN (Print)9781728161198
DOIs
Publication statusPublished - 2 Dec 2019
EventInternational Conference on Machine Learning and Data Engineering 2019 - Taipei, Taiwan, Province of China
Duration: 2 Dec 20194 Dec 2019

Publication series

NameProceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019

Conference

ConferenceInternational Conference on Machine Learning and Data Engineering 2019
Abbreviated titleiCMLDE 2019
CountryTaiwan, Province of China
CityTaipei
Period2/12/194/12/19

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  • Cite this

    Chetty, G., Bui, H., & White, M. (2019). Deep learning based spam detection system. In P. K. Rhee, K-Y. Hwa, T-W. Pai, D. Howard, & R. Bashar (Eds.), Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019 (pp. 91-96). [8995757] (Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/iCMLDE49015.2019.00027