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Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam

Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam

Hadj Ahmed Bouarara
Copyright: © 2018 |Pages: 37
ISBN13: 9781522549444|ISBN10: 1522549447|EISBN13: 9781522549451
DOI: 10.4018/978-1-5225-4944-4.ch011
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MLA

Bouarara, Hadj Ahmed. "Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam." Global Implications of Emerging Technology Trends, edited by Francisco José García-Peñalvo, IGI Global, 2018, pp. 179-215. https://doi.org/10.4018/978-1-5225-4944-4.ch011

APA

Bouarara, H. A. (2018). Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam. In F. García-Peñalvo (Ed.), Global Implications of Emerging Technology Trends (pp. 179-215). IGI Global. https://doi.org/10.4018/978-1-5225-4944-4.ch011

Chicago

Bouarara, Hadj Ahmed. "Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam." In Global Implications of Emerging Technology Trends, edited by Francisco José García-Peñalvo, 179-215. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-4944-4.ch011

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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 chapter unveils 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 experimentation, the authors used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy, and error). They optimize 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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