Email Spam Detection and Malware Filtering using Machine Learning

Spam and malware pose a threat to Internet users as they can lead to the loss of sensitive information, financial losses, and privacy breaches. Modern spam filtering and malware detection methods are becoming less and less effective as spammers and malware authors continue to develop new techniques to bypass them. Machine learning holds great promise for these problems. This paper proposes a machine learning-based approach to detect spam and filter out malware. This method is evaluated using information about spam and nonspam emails.By comparing the results of various classification algorithms, the Naive Bayesmethod has been shown to be very effective in detecting spam and malware.