Phishing URL Detection Using Machine Learning Techniques
Phishing URL detection is an important task for security defenders. Phishing websites impersonate other websites, luring victims into malicious web pages. Detecting phishing domains is a complex and dynamic problem. To address this problem, a number of machine learning techniques have been proposed. These methods extract features from URLs, website traffic, and third-party services.
An effective detection approach for phishing websites using …
These features are interpreted to identify the significant characteristics of phishing URLs. Some of these features include character level sequences, the presence of hyphens, and the number of slashes. These characteristics are not present in benign websites. These features also help in locating information that is sensitive.
Another feature to look for in a phishing URL is the presence of a subdomain name. A subdomain name allows the phisher to have full control over the website and is a key indicator of a phishing attack. The attacker can choose a domain name that is long, confusing, or contains irrelevant brand names.
In addition to the features described above, a number of researchers have proposed image-based approaches and methods for detecting phishing webpages. To identify phishing webpages, these authors used variables from financial institutions and web image properties. They also computed the distance of webpage image signatures.
Another method to detect phishing URLs uses an association rule mining technique. This technique allows the researcher to identify useful features from phishing URLs. These features are then combined with blacklisted domains.
Some of the features used in these techniques include structural, lexical, and third-party-generated features. This technique is useful for reducing misclassification ratio.