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How to Use Machine Learning Against Email Phishing
Phishing protection mechanisms need to work to minimize the response time to new scam strategies to make attacks unprofitable.
Fremont, CA: Phishing is one of the most fraudulent attacks which is found in malicious email traffic where a recipient will receive an email in the form of a message from a distinguished company that will require them to follow a link and login to a service or enter card details on a fake web page.
However, as phishing techniques develop, detection methods are also advancing quickly. Attackers will confront as long as their attempts are profitable. As such, phishing protection mechanisms need to work to minimize the response time to new scam strategies to make attacks unprofitable.
Manually-created dictionaries were used to identify phishing in email traffic about ten years ago, describing all of the possible variants that phishing texts may include. Heuristics were then introduced among the detection technologies. Heuristics search for signs that identify the nature of the message and how it might be dangerous.
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Analyzing email headers is another method of detection. A header includes information about the email, its sender, and route to the recipient, such as data of creation, ID number, encoding type, mailing address, and IP address, which is visible in a message's properties. Examining the headers help cybersecurity specialists detect suspicious senders.
A single phishing indicator does not help identify and be sure if the message is malicious or not. Instead, specialists look for sets of indicators that are combined into signatures that can uniquely detect if a message is malicious.
Today, an increasing number of attacks are being carried out by masking their malicious messages as emails from new online services, taking advantage of popular events. To safeguard users from such attacks, the need for technology that can quickly identify new types of phishing emails through automation is required. This technology utilizes statistics and machine learning, allowing automatic extraction of the necessary information to identify and prevent phishing and train and retrain itself as required.