Unable to Detect New Techniques
Text messaging is a common target for malicious attacks, but the existing monitoring approach relied on legacy tactics and static rules, lacking the flexibility to adapt to evolving and more sophisticated spamming or phishing techniques.
Resource Intensive Approach
Manual review of flagged text messages is driving high resource consumption and operational costs.
Created a machine learning model to help better detect spam and phishing within text messages.
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Implemented an automated classification approach for better predicting whether something is SMS spam.
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Events were organized into distinct categories, and corresponding reports were generated.
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We deployed a machine learning model for spam and phishing detection that achieved 93% accuracy, significantly reducing false positives and substantially lowering the internal resources needed for manual reviews.
The new model continuously learns and adapts from data in an unsupervised manner, eliminating the need for static rules and thresholds while staying responsive to emerging spamming and phishing techniques.
Contact us today to learn how we can help ensure success in your upcoming projects.
