BIRCH and DB-scan Techniques in Phishing and Malware Detection

Malware and phishing detection is one of the most fascinating topics in recent era because of the harm produced by them to the internet users. Phishing website detection can be said as new to the arena. Phishing websites are considered as one of the lethal weapon to embezzle one’s personal information and use it for the crackers benefits. In spite of the fact that malware samples and phishing websites share common attributes, they are created and unleashed to the common world in thousand per day. Since its entry to the internet world, detecting the phishing websites and malwares samples is been a tremendous test to the internet security experts. Many clustering techniques have been deployed to tear apart the phishing websites and the malware samples. The Detection course has been divided into two steps 1) Feature Extraction, where the ideal features are extracted to capture the nature of the files samples and the phishing websites. 2) Categorization, where exceptional techniques are used to automatically group the file samples and the websites into different classes. In this paper, we develop an automatic categorization system to class Malware samples and phishing websites using a cluster ensemble by combining the clustering solutions provided by different base clustering algorithms. Keywords— Malware samples; phishing websites; Feature Extraction; Categorization; Base Clustering algorithms