This technology uses advanced technologies, such as AI, Natural Language Processing, image processing, and heuristics, to detect and prevent attempts of brand impersonation by matching URLs and web pages with established brands.
Our new DeepBrand Clustering technology is the next evolution of Brand Spoofing Prevention, designed to keep up with the growing number of websites and spoofed pages.
Identifying and indexing every brand on the internet is an unsustainable task aimed at finding a needle in a constantly expanding haystack.
The volume of brand websites makes detecting brand spoofing challenging, leaving many attempts undetected and exposing consumers and businesses to fraud and cyberattacks.
There's a pressing need for automated, intelligent systems that can adapt and scale with the growing digital brand ecosystem.
A major challenge in detecting brand spoofing scams is labeling data needed to train the relevant AI models.
This requires identifying diverse brand elements and understanding nuanced differences between them.
To tackle data labeling, we turned to unsupervised learning, automatically attributing web page characteristics to brands.
This approach reduces reliance on human intervention, saving time and minimizing errors in brand element identification.
The neural network trains on unlabeled traffic in order to learn to identify brands automatically and without supervision, based on common characteristics in the web page, such as domain, favicon, title, and more.
These steps range from extracting brand indicators to automatically assigning brand names to clusters.
Once data is gathered and standardized The output of the entire pipeline is a trained model with multiple distinct clusters and assigned brand names, the model organizes web pages into clusters associated with specific brands, and each cluster is labeled accordingly.
These clusters, particularly the most distinct ones, are utilized to analyze real-time traffic and identify brand presence.
The engine evaluates whether the activity signifies a potential malicious brand spoofing attempt.
This technique represents a significant leap forward in brand protection technology.
The entire system is patent pending, underscoring its novel approach and the advanced capabilities it brings to the challenge of brand spoofing detection.
Within several hours of running the learning phase, DeepBrand clustering indexed more than 4000 distinct brands.
Out of the observed brands, more than 200 unique brands were spoofed in more than 4000 malicious attacks.
The landscape of brand spoofing attacks is constantly evolving, with new threats emerging frequently.
DeepBrand Clustering's enhanced detection capabilities allow it to be at the forefront, often identifying brand spoofing attacks before they are even known and added to databases like VirusTotal.
This Cyber News was published on blog.checkpoint.com. Publication date: Tue, 02 Jul 2024 14:13:05 +0000