Phishing attacks are designed to steal sensitive data like passwords, credit card numbers, network credentials and more. To evade traditional defenses, attackers use a wide range of tools to disguise their activities and mislead unsuspecting users. To combat phishing, businesses need sophisticated tools that can detect phishing attacks in real-time and speed up response times.

Real-time phishing detection uses ML to identify suspicious indicators of a phishing attack and trigger alerts. By analyzing data from multiple sources, it can spot anomalies and patterns that would be missed by traditional methods. This allows security teams to identify phishing attempts quickly, and respond faster — which helps reduce the potential impact on business operations and brand reputation.

Real-Time Phishing Detection: Stay Ahead of the Threats

For example, ML can analyze email headers to identify spoofed sender information and other irregularities that suggest an email may be phishing. It can also use image analysis to scan websites for indicators of phishing, such as distorted logos or banner images. It can even look at user behavior, such as click patterns and mouse movements, to identify abnormal activity that might indicate a phishing attempt.

However, ML-based models require a huge volume of training data to achieve high accuracy and performance. This means that it can be difficult to maintain the model’s performance in different data sets. Also, phishing techniques are always changing as cybercriminals find new ways to circumvent security measures. This can cause a phenomenon known as concept drift, where previously trained models become less effective over time.