The constant growth of phishing and the rise in the number of phishing websites that could not be detected by search engines have led to the fact that individuals and organizations around the world are increasingly exposed to various cyber attacks. Phishing attacks are one of the most common and least protected security threats these days in the form of sealed browsers. We have techniques that are used to analyze textual URLs and identify indirect claims that indicate phishing attacks. Consequently, more effective phishing detection is required for improved cyber defense. Phishing attackers always use new and sophisticated techniques to deceive online customers. Hence, it is necessary that the anti-phishing solution should be an intelligent real-time system and fast. Threat intelligence and behavioral analytics systems support organizations to prevent phishing attacks. Our approach is novel compared to previous works; we collect phishing websites through various browsers using mini-bots or crawlers. The result will be used as the main source for linguistic analysis of the contents used for malicious attacks. A model will be built that involved in phishing detection and those websites will be avoided in real-time environments.