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Phishing links are links that are used for fraudulent purposes such as stealing private information or infecting a host with spyware. They are normally transmitted through electronic mail and are generally disguised as a legitimate website. Detecting these links is becoming increasingly difficult.

In order to detect these malicious links, researchers have developed several techniques using various machine learning algorithms. Machine learning is a branch of artificial intelligence that studies text and identifies patterns. Using these techniques, phishing link detection is possible.

Phishing links are malicious HTML codes that imitate the URL address of a real website. These links are usually disguised as a bank site and steal account numbers or passwords. Hence, they are often regarded as a social engineering attack.

Several security companies maintain a blacklist of known phishing websites. This list contains domain names, keywords, and certificates. It is a must to keep updated this list because phishing attacks are changing in nature.

Traditional machine learning methods require extensive time to train and select features. However, they also reduce the classification efficiency. Hence, an effective technique is required to regulate retraining and maintain accuracy.

Another approach is a fishing link detection method, which matches suspicious URLs with a phishing URL blacklist. This approach has the added advantage of being able to adapt to changes in phishing patterns.

A more robust approach involves incorporating machine learning algorithms into the phishing link detection model. In particular, the paper discusses a multi-feature hybrid machine learning model.

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