![]() Using 10-fold cross-validation, the experimental results indicate a remarkable performance with 0.04% False Positive and 99.96% accuracy for both SVM and NB predictive models.ĭetecting phishing attacks (identifying fake vs. The experiments were based on datasets consisting of 2541 phishing instances and 2500 benign instances. ![]() The proposed system uses Support Vector Machine and Naïve Bayes which have been trained on a 15-dimensional feature set. These features are extracted from the URL, webpage properties and webpage behaviour using the incremental component-based system to present the resultant feature vector to the predictive model. The predictive model consists of Feature Selection Module which is used for the construction of an effective feature vector. In this work, an enhanced machine learning-based predictive model is proposed to improve the efficiency of anti-phishing schemes. Although Machine Learning approaches have achieved promising accuracy rate, the choice and the performance of the feature vector limit their effective detection. ![]() Despite the availability of myriads anti-phishing systems, phishing continues unabated due to inadequate detection of a zero-day attack, superfluous computational overhead and high false rates. Nowadays, many anti-phishing systems are being developed to identify phishing contents in online communication systems. ![]()
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