Abstract
In order to effectively detect phishing attacks, this paper proposes a method of combining Particle Swarm Optimization with BP neural network to build a new phishing website detection system. PSO optimizes neural network parameters to improve the convergence performance of neural network detection model. Experimental results show that this algorithm can improve the prediction speed and the accuracy of detecting phishing websites by 3.7% compared with the conventional BP neural network algorithm.
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Acknowledgments
The authors thank all the reviewers and editors for their valuable comments and works. This paper is supported by National Key Research and Development Program of China (Grant No. 2017YFB0802000), National Natural Science Foundation of China (Grant Nos. U1636114, 61772550, 61572521), Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2016JQ6037).
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Chen, W., Wang, X.A., Zhang, W., Xu, C. (2018). Phishing Detection Research Based on PSO-BP Neural Network. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_91
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DOI: https://doi.org/10.1007/978-3-319-75928-9_91
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