Detecting phishing attacks in real-time is becoming an increasingly important part of protecting users. Many security teams and platform developers are in need of robust URL scanning mechanisms that can operate at the speed and volumes needed to prevent phishing and scams from infecting platforms. Bolster’s phishing detection API gives you the power to detect and block fraudulent links and domains in real-time.
A phishing attack is an attempt to steal sensitive information from a user. This can be in the form of email account passwords or financial data. Phishers use fake websites to mimic legitimate web pages and lure users into providing their credentials. The attackers may also attempt to install malware on a computer or mobile device.
To protect against phishing, various technologies are being developed. Some of these technologies include user profile filters, browser-based toolbars and phishing detection algorithms. These tools monitor and store a list of phishing websites on a user’s machine. When a phishing website is encountered, these tools warn the user and redirect them to another site.
These technologies have a number of limitations. First, they require crawling and analyzing the content of the targeted websites. They also rely heavily on network environments and third-party services. Moreover, they are susceptible to technology changes and are not robust enough to detect new threats.
Another major limitation of these technologies is that they can’t identify phishing domains that aren’t listed on blocklists like Google SafeBrowsing, PhishTank and SmartScreen. This means that a user could be entering into an authentic looking website with no warning. This can lead to critical data loss and the introduction of malware.
Despite these limitations, some phishing-detection technologies are still widely used by security teams. These techniques work by monitoring websites that are visited on a regular basis and alerting the user when they see an unfamiliar website. This is useful for identifying phishing sites that are based on a specific pattern such as a long or multiple-dots URL.
A phishing detection algorithm is a machine learning model that learns to distinguish phishing websites from legitimate ones. The algorithms typically evaluate a large set of URL syntax features. The most common feature used to identify phishing websites is the presence of a URL.
The majority of these algorithms are designed to identify phishing websites that are similar to legitimate ones. However, some models are designed to identify phishing websites that use non-standard patterns. For example, some phishing-detection models are designed to identify sites that have an IP address in the URL or are too long.
This type of phishing-detection is prone to false positives since a phishing site can have similar naming characteristics to a legitimate website. For this reason, it is necessary to use a phishing site as a training dataset to train the model.
Other phishing-detection algorithms rely on a single metric, such as domain in HTML or the use of specific technologies. These metrics are important for detecting phishing sites because they indicate whether a site is phishing or not.