Phishing website prediction using base and ensemble classifier techniques with cross-validation

Abstract

Internet or public internetwork has become a vulnerable place nowadays as there are so many threats available for the novice or careless users because there exist many types of tools and techniques being used by notorious people on it to victimize people somehow and gain access to their precious and personal data resulting in sometimes smaller. However, these victims suffer considerable losses in many instances due to their entrapment in such traps as hacking, cracking, data diddling, Trojan attacks, web jacking, salami attacks, and phishing. Therefore, despite the web users and the software and application developer's continuous effort to make and keep the IT infrastructure safe and secure using many techniques, including encryption, digital signatures, digital certificates, etc. this paper focuses on the problem of phishing to detect and predict phishing websites URLs, primary machine learning classifiers and new ensemble-based techniques are used on 2 distinct datasets. Again on a merged dataset, this study is conducted in 3 phases. First, they include classification using base classifiers, Ensemble classifiers, and then ensemble classifiers are tested with and without cross-validation. Finally, their performance is analyzed, and the results are presented at last to help others use this study for their upcoming research.