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New Bio Inspired Techniques in the Filtering of Spam: Synthesis and Comparative Study

New Bio Inspired Techniques in the Filtering of Spam: Synthesis and Comparative Study

Hadj Ahmed Bouarara, Reda Mohamed Hamou, Abdelmalek Amine
Copyright: © 2016 |Volume: 9 |Issue: 2 |Pages: 31
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781466689831|DOI: 10.4018/JITR.2016040103
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MLA

Bouarara, Hadj Ahmed, et al. "New Bio Inspired Techniques in the Filtering of Spam: Synthesis and Comparative Study." JITR vol.9, no.2 2016: pp.47-77. http://doi.org/10.4018/JITR.2016040103

APA

Bouarara, H. A., Hamou, R. M., & Amine, A. (2016). New Bio Inspired Techniques in the Filtering of Spam: Synthesis and Comparative Study. Journal of Information Technology Research (JITR), 9(2), 47-77. http://doi.org/10.4018/JITR.2016040103

Chicago

Bouarara, Hadj Ahmed, Reda Mohamed Hamou, and Abdelmalek Amine. "New Bio Inspired Techniques in the Filtering of Spam: Synthesis and Comparative Study," Journal of Information Technology Research (JITR) 9, no.2: 47-77. http://doi.org/10.4018/JITR.2016040103

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Abstract

The internet era promotes electronic commerce and facilitates access to many services. In today's digital society the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This paper deals on the unveiling of fresh bio-inspired techniques (artificial social cockroaches (ASC), artificial haemostasis system (AHS) and artificial heart lungs system (AHLS)) and their application for SPAM detection. For the authors' experimentation, they have used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy and error). They have optimising the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine learning algorithms (decision tree C4.5 and K-means).

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