Deep learning based spam detection system

Girija Chetty; Hieu Bui; Matthew White

doi:10.1109/iCMLDE49015.2019.00027

Deep learning based spam detection system

Girija Chetty, Hieu Bui, Matthew White

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

12 Citations (Scopus)

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 language	English
Title of host publication	Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019
Editors	Phill Kyu Rhee, Kuo-Yuan Hwa, Tun-Wen Pai, Daniel Howard, Rezaul Bashar
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	91-96
Number of pages	6
ISBN (Electronic)	9781728104041
ISBN (Print)	9781728161198
DOIs	https://doi.org/10.1109/iCMLDE49015.2019.00027
Publication status	Published - 2 Dec 2019
Event	International Conference on Machine Learning and Data Engineering 2019 - Taipei, Taiwan, Province of China Duration: 2 Dec 2019 → 4 Dec 2019

Publication series

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

Conference

Conference	International Conference on Machine Learning and Data Engineering 2019
Abbreviated title	iCMLDE 2019
Country/Territory	Taiwan, Province of China
City	Taipei
Period	2/12/19 → 4/12/19

Access to Document

10.1109/iCMLDE49015.2019.00027

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

Chetty, Girija ; Bui, Hieu ; White, Matthew. / Deep learning based spam detection system. Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019. editor / Phill Kyu Rhee ; Kuo-Yuan Hwa ; Tun-Wen Pai ; Daniel Howard ; Rezaul Bashar. United States : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 91-96 (Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019).

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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.",

keywords = "Deep learning, Spam detection, Word embedding",

author = "Girija Chetty and Hieu Bui and Matthew White",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; International Conference on Machine Learning and Data Engineering 2019, iCMLDE 2019 ; Conference date: 02-12-2019 Through 04-12-2019",

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doi = "10.1109/iCMLDE49015.2019.00027",

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series = "Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019",

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Chetty, G, Bui, H & White, M 2019, Deep learning based spam detection system. in PK Rhee, K-Y Hwa, T-W Pai, D Howard & R Bashar (eds), Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019., 8995757, Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 91-96, International Conference on Machine Learning and Data Engineering 2019, Taipei, Taiwan, Province of China, 2/12/19. https://doi.org/10.1109/iCMLDE49015.2019.00027

Deep learning based spam detection system. / Chetty, Girija; Bui, Hieu; White, Matthew.
Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019. ed. / Phill Kyu Rhee; Kuo-Yuan Hwa; Tun-Wen Pai; Daniel Howard; Rezaul Bashar. United States: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 91-96 8995757 (Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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AU - White, Matthew

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N2 - 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.

AB - 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.

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KW - Spam detection

KW - Word embedding

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

Deep learning based spam detection system

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