how would you implement a successful spam filtering policy?
Emails are an important part and parcel of an organization where all the communication are totally based on the emails. But with this, attackers have found a way to gather data from the emails and hacking the email account itself. By using spam and phishing emails, your credentials will get shared with the attacker and then the attacker can exploit your data. So, it is really important to put or implement spam filtering policy in order to secure your infrastructure from these type of spam attacks.
The implementation of successful spam filter policy in your exchange server is very important and it must be made sure that a reporting mechanism is there before the implementation. Once the filtering policy is applied, the report should capture the statistics about the incoming spam emails. Spam filtering policy includes the filter that will check incoming emails and also check the sender whether the sender is in black list or white list. Based on that the policy will decide whether the emails are spam or ham. Secondly, the policy will also check for image-based e-mails and then take a decision based on the images attached with the mail. Successful spam filtering policy should eliminate the spam emails from entering your mailbox.
Problem 5. (12 points) Bayes' Theorem is a popular tool for spam filtering. You are asked to design a spam filtering algorithm based on whether certain words appear in an email. You were given 1,000 randomly selected emails that entered an email server. You examined these emails and manually labeled each one either as spam or ham (i.e., non-spam). You found that 400 emails are spam and 600 are ham. In these 400 spam emails, you found 200 of thenm...
Problem 5. (12 points) Bayes' Theorem is a popular tool for spam filtering. You are asked to design a spam filtering algorithm based on whether certain words appear in an email. You were given 1,000 randomly selected emails that entered an email server. You examined these emails and manually labeled each one either as spam or ham (i.e., non-spam). You found that 400 emails are spam and 600 are ham. In these 400 spam emails, you found 200 of them...
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