Abstract
Nowadays, email spam problems continue growing drastically and many spam detection algorithms have been developed at the same time. However, there are several shortcomings shared by most of these algorithms. In order to solve these shortcomings, we present an advanced spam detection technique(ASDT). It is based on the extremum characteristic theory, Rabin fingerprint algorithm, modified Bayesian method and optimization theory. Then we designed several experiments to evaluate ASDT’s performance, including accuracy, speed and robustness, by comparing them with SFSPH, SFSPH-S, the famous DSC algorithm and the Email Remove-duplicate Algorithm Based on SHA-1(ERABS). Our extensive experiments demonstrated that ASDT has the best accuracy, speed and robustness on spam filtering.
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Zhu, J., Li, A. (2014). An Advanced Spam Detection Technique Based on Self-adaptive Piecewise Hash Algorithm. In: Han, W., Huang, Z., Hu, C., Zhang, H., Guo, L. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8710. Springer, Cham. https://doi.org/10.1007/978-3-319-11119-3_14
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DOI: https://doi.org/10.1007/978-3-319-11119-3_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11118-6
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