Meta-Algorithms for Improving Classification Performance in the Web-phishing Detection Process

Web phishing is one of the many crimes that occur in cyberspace and often threatens internet users around the world. Web phishing works by tricking the victim into a website page that has been designed to resemble the original page and then directing the target to submit the important information they have. Web phishing detection system needs to be developed to minimize attacks and theft of information using the website. Research related to web phishing detection system has been carried out by many researchers, one of them using data mining techniques, but still uses a single classification algorithm. Therefore, the addition of meta-algorithm is proposed to support the improvement of classification performance for the development of various web phishing detection systems. From the testing phase that conducted using Web Phishing dataset from UCI Machine Learning Repository, an increase in accuracy value of 97.1% is obtained by the addition of the bagging process, 97.3% by using the boosting process, and 97.5% by the addition of the stacking process. With the resulting improved performance, it is hoped that the model can be used as a reference in perfecting the development of various phishing web detection systems.