In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones...
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In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.
Background: Metagenomics [1], a burgeoning subject of studying microbes by sequencing environmental samples directly, is proposed on account of the vast majority of microbes (99%) that cannot be ***, a metagenomic sam...
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Background: Metagenomics [1], a burgeoning subject of studying microbes by sequencing environmental samples directly, is proposed on account of the vast majority of microbes (99%) that cannot be ***, a metagenomic sample is a heterogeneous mixture of fragments that originate from different microbial genomes.
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