Case Studies


Data Lake and Brand Protection Predictive Model

Data Lake with 100 TB of storage with 7TB of data being ingested and processed weekly with a 2-class boosted Decision Tree with 87.3% Accuracy

Business Case

Our client is a leading service provider of digital brand services to leading business globally. They have a huge responsibility of protecting their customers’ brands and trademarks from various phishing channels. The client wanted a streamlined mechanism to gather, analyze and report this data on a day-to-day basis.


Business Benefits

  • Automated anti-phishing service: Helps in identifying the phishing content through multiple sources such as feeds, emails from customers. The solution supports fraud prevention using a takedown process and monitors them after takedowns. Protects end-users, by taking action against phishing fraudsters who steal sensitive information for misuse.
  • Internet Monitoring Service: Police the internet – Strengthen customer brand’s credibility and Value.
  • Domain Name Monitoring: Monitor domain registrations of exact matches, variants. Further, typos are identified and reports are sent to the customers for further action.
  • Image Match: Compare the web images with customer images to identify any potential infringement or misuse of the customers’ images related to the business.
  • Analyst view for all customer-facing data. There is another eye watching the results, chances are very less for admitting mistakes in the end results and reduce the number of errors/erroneous data.
  • Improved customer features (self-service) and reduces the dial-in calls by the customers thereby achieving end-user satisfaction.
  • Statistics
    • Processed 800 Million records
    • On an average 250 sites are shut down due to this process across the globe, which is the number of average abuses.

Implementation Details

  • Gather and process data related to phishing of brands, trademark, domains etc.
  • Process unstructured data and bring it to a structured shape.
  • Leverage an anti-phishing algorithm to assign proximity score and filter the probable phishing cases/URLs.
  • Report final set of data to the agents (on a web portal) to confirm further and take next course of action manually.
  • Implement Big Data solution to handle high volume and unstructured data.

Platform Details

  • Private Data Center used
  • 20 node cluster with 100 GB RAM, 12 core, 5TB
  • Cloudera 5.8, Hadoop 2.x
  • Solr, SonicMQ, and Oracle

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