Mining Web to Detect Phishing URLs

Proliferation of phishing attacks in recent years has presented an important cyber security research area. Over the years, there has been an increase in the technology, diversity, and sophistication of these attacks in response to increased user awareness and countermeasures. In this paper, we propose a novel scheme to automatically detect phishing URLs by mining and extracting Meta data on URLs from various Web services. Applying the proposed approach on real-world data sets, it is demonstrated that Logistic Regression classifier can achieve an overall accuracy of 97.2-99.8%, false positive rate of 0.1-1% and false negative rate of 0.7-6.5% in detecting phishing and non-phishing URLs.