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Malay Language Text-Based Anti-Spam System Using Neural Network

Malay Language Text-Based Anti-Spam System Using Neural Network

Hamid A. Jalab, Thamarai Subramaniam, Alaa Y. Taqa
Copyright: © 2015 |Pages: 12
ISBN13: 9781466665835|ISBN10: 1466665831|EISBN13: 9781466665842
DOI: 10.4018/978-1-4666-6583-5.ch013
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MLA

Jalab, Hamid A., et al. "Malay Language Text-Based Anti-Spam System Using Neural Network." Handbook of Research on Threat Detection and Countermeasures in Network Security, edited by Alaa Hussein Al-Hamami and Ghossoon M. Waleed al-Saadoon, IGI Global, 2015, pp. 242-253. https://doi.org/10.4018/978-1-4666-6583-5.ch013

APA

Jalab, H. A., Subramaniam, T., & Taqa, A. Y. (2015). Malay Language Text-Based Anti-Spam System Using Neural Network. In A. Al-Hamami & G. Waleed al-Saadoon (Eds.), Handbook of Research on Threat Detection and Countermeasures in Network Security (pp. 242-253). IGI Global. https://doi.org/10.4018/978-1-4666-6583-5.ch013

Chicago

Jalab, Hamid A., Thamarai Subramaniam, and Alaa Y. Taqa. "Malay Language Text-Based Anti-Spam System Using Neural Network." In Handbook of Research on Threat Detection and Countermeasures in Network Security, edited by Alaa Hussein Al-Hamami and Ghossoon M. Waleed al-Saadoon, 242-253. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6583-5.ch013

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Abstract

This unauthorized intrusion has cost time and money for businesses and users. The exponential growth of spam emails in recent years has resulted in the necessity for more accurate and efficient spam filtering. This chapter focuses on creating a text-based anti-spam system using back-propagation neural network for Malay Language emails that efficiently and effectively counter measure spam problems. The proposed algorithm consists of three stages; pre-processing, implementation and evaluation. Malay language emails are collected and divided into spam and non-spam. Features are extracted and document frequency as dimension reduction technique is calculated too. Classifiers are trained to recognize spam and non-spam emails using training datasets. After training, classifiers are tested to check whether they can predict spam (or non-spam) emails accurately with the testing datasets. The result of this classification in terms of accuracy, precision, and recall are evaluated, compared and analyzed, thus providing the best anti-spam solution to counter measure spam problem of Malay language emails.

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