Detecting Fraudulent Shopping Websites Using Website Categorization API

Arizona State University and WhoisXML API: Enhancing Online Scam Detection and Protection with Website Categorization API

About

Marzieh Bitaab, a PhD student in Computer Science at Arizona State University, developed a system to fight cybercrime and make the online shopping environment safer for consumers. The system addresses the growing volume of fraudulent shopping websites by accurately analyzing content and detecting potential scams using a neural network-based classifier.

Location

Industry

Cybersecurity

Products used

Website Categorization API

Highlights

Existing detection systems cover areas that are too broad to target fraudulent online shopping sites specifically.
Reliable website categorization leads to fast and accurate shopping website classification for the data pipeline.
The researcher’s scam detection classifier was able to detect scam shops more accurately.

Challenge

Targeted Detection of Online Shopping Scams

The first step toward protecting consumers from fraudulent shopping websites is identifying and classifying sites among a vast array of websites and website categories. However, after rigorous testing, Ms. Bitaab found that widely used mitigation methods, such as Google Safe Browsing and phishing detection systems, did not accurately and precisely detect online shopping scams.

The researcher realized the need to create a new solution that collects domains daily, classifying them, filtering out unrelated ones, and analyzing the content of websites categorized as shopping.

Solution

Reliable and Fast Website Categorization API

The researcher used Website Categorization API to aid in her daily web classification process, allowing her to design efficient data collection pipelines for the online shopping category.

Website Categorization API’s accuracy and fast inference speed enabled the researcher to classify domains and filter out those unrelated to shopping. The API’s confidence scores for each website’s categories further allowed the researcher to set desired thresholds for better accuracy, ensuring the system can adapt based on unique confidence requirements.

Marzieh Bitaab
PhD Student, Computer Science
Arizona State University
“Thanks to well-structured and clear API documentation provided by WhoisXML API, we were able to integrate the solution seamlessly into our system with minimal technical challenges.”

Results

More Efficient Data Pipelines and Effective Online Shopping Fraud Detection

Accurate Detection of Shopping Websites

Website Categorization API is instrumental in the project’s primary process, which involves collecting domains daily, classifying them, and picking out only shopping websites.

Efficient Data Pipelines

Website Categorization API enabled Ms. Bitaab to optimize the designed data pipelines and focus solely on shopping websites. That translates to a smaller amount of data that needs to be processed, making the system faster.

Improved Scam Detection Classifier Accuracy

The inclusion of non shopping websites in the data pipeline would have decreased the accuracy of the scam detection system since their features differ from those of shopping websites for which the system was designed. By filtering out unrelated sites, Website Categorization API was able to help train and enhance the system’s accuracy.

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