International Journal of Communications, Network and System Sciences

Volume 9, Issue 1 (January 2016)

ISSN Print: 1913-3715   ISSN Online: 1913-3723

Google-based Impact Factor: 0.66  Citations  h5-index & Ranking

Optimizing Feedforward Neural Networks Using Biogeography Based Optimization for E-Mail Spam Identification

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DOI: 10.4236/ijcns.2016.91002    5,578 Downloads   7,195 Views  Citations

ABSTRACT

Spam e-mail has a significant negative impact on individuals and organizations, and is considered as a serious waste of resources, time and efforts. Spam detection is a complex and challenging task to solve. In literature, researchers and practitioners proposed numerous approaches for automatic e-mail spam detection. Learning-based filtering is one of the important approaches used for spam detection where a filter needs to be trained to extract the knowledge that can be used to detect the spam. In this context, Artificial Neural Networks is a widely used machine learning based filter. In this paper, we propose the use of a common type of Feedforward Neural Network called Multi-Layer Perceptron (MLP) for the purpose of e-mail spam identification, where the weights of this network model are found using a new nature-inspired metaheuristic algorithm called Biogeography Based Optimization (BBO). Experiments and results based on two different spam datasets show that the developed MLP model trained by BBO gets high generalization performance compared to other optimization methods used in the literature for e-mail spam detection.

Share and Cite:

Rodan, A. , Faris, H. and Alqatawna, J. (2016) Optimizing Feedforward Neural Networks Using Biogeography Based Optimization for E-Mail Spam Identification. International Journal of Communications, Network and System Sciences, 9, 19-28. doi: 10.4236/ijcns.2016.91002.

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