Cybercrime has emerged as a specialized domain, leveraging online communication networks with advanced specifications to identify cybercriminals through the application of cyber laws. Extensive research is underway to establish pertinent legal methodologies aimed at preventing and controlling cybercriminal activities. Over the past decade, considerable attention has been devoted to the compelling topics of malware and phishing detection, given the substantial damage inflicted upon internet users. Phishing website recognition represents recognizing these sites as potent tools for exploiting personal information and facilitating malicious activities. This paper introduces an innovative automatic categorization system designed to classify both malware identities and phishing websites. The system achieves this through the integration of clustering solutions, incorporating various clustering algorithms within a cluster ensemble scheme.