Malicious URLs are a common problem on the Internet. They can be used to install Trojans on victims’ computers, or to leak sensitive information. The best way to avoid them is to be aware of what you are clicking, and to click smartly.
Detect malicious URLs is difficult and time-consuming, as attackers use multiple techniques to mask their malicious intent. However, detecting malicious URLs is vital to keeping your network safe from hackers.
The traditional approach to detecting malicious URLs is to build blacklists, which are databases that store lists of malicious domain names and prevent them from being browsed. These lists are maintained by security experts and updated regularly.
However, these lists can be outdated as new malicious domains are frequently generated algorithmically by attackers. This leads to a serious challenge for traditional blacklisting methods.
The Anatomy of a Malicious URL: How to Spot and Avoid Phishing and Malware Attacks
To overcome this issue, many researchers have proposed methods that leverage machine learning to detect malicious URLs. These approaches often use a combination of lexical features, host information, and domain name features.
These features can help improve the detection accuracy and reduce the false alarm rate. They also allow the detection model to learn the characteristics of a malicious URL and distinguish it from a benign one.
The model presented here aims to provide an effective and scalable method for detecting malicious URLs. It employs a Cyber Threat Intelligence-based approach that consists of seven phases: data collection, feature preprocessing, feature extraction, feature representation, ensemble learning-based prediction, and decision making.