Check Point Enhances Zero-Phishing AI Engine to Combat Brand Spoofing Attacks

Editorial Desk
By Editorial Desk 2 Min Read

Check Point Software Technologies has enhanced its Zero-Phishing AI Engine to combat brand spoofing attacks.

The improved engine can now identify and block attempts to impersonate local and global brands across multiple languages and countries.

By inspecting new domains immediately upon registration, potential brand spoofing attempts are blocked before they can be used in attacks.

Brand spoofing is a prevalent method used by cybercriminals to deceive individuals and steal personal information or payment credentials. Popular brands like Microsoft, Google, LinkedIn, Wells Fargo, and Walmart are frequently imitated in phishing incidents.

Attackers send fraudulent emails or text messages containing links to fake websites that closely resemble the official sites of these brands. The fake websites often trick users into entering their credentials or personal information, which is then stolen by the attackers.

Check Point’s research teams found that in 2022, 21% of initial entry vectors in cyberattacks were due to phishing incidents.

These attacks targeted not only global brands but also local brands, such as banks, financial services, post offices, and government websites. Attackers exploit the trustworthiness of these brands to deceive their targets successfully.

To defend against brand spoofing attacks, Check Point has developed an innovative AI-Powered engine that can detect and block impersonation attempts of both local and global brands.

This engine utilizes Machine Learning, Natural Language Processing, and Image Processing to analyze the structure and content of websites and identify indications of brand spoofing.

The new engine also incorporates Pre-Emptive Prevention capabilities, where it scans newly registered domains to detect potential brand spoofing attempts.

Any spoofed domains are stored in Check Point’s ThreatCloudAI, enabling proactive protection across all Check Point products. This collaborative intelligence blocks access to links in emails, files, messaging platforms, and web browsing.

The engine’s classification process involves extracting features from URLs or web page content, deriving the brand context using NLP and AI, and running the data through a classification layer for a final determination.

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