Nowadays, one of the cheapest forms of communiqué in the globe is email, and its ease makes it vulnerable to lots of threats. One of the most imperative threats to email is spam; unwanted email, especially when promotion agency send a throng mail. Spam email could also contain malware as scripts or further executable file. Occasionally they also include unsafe attachment or links to phishing websites. These cruel spams threaten the privacy and security of huge amount of sensitive data. Therefore, a system that is able to automatically learn how to categorize malicious spam in email is extremely desirable. In this paper, we aim to develop finding of malicious spam throughout feature selection. We suggest models that employ a novel dataset for the procedure of feature selection, a pace for improving categorization in later stage. Feature selection is predictable to develop training time and precision of malicious spam detection. This paper too shows the evaluation of various classifiers use during the process....
Authors: Prabha Pandey, Chetan Agrawal, Tehreem Nishat Ansari.