Spam Detection Using Machine Learning

Customer

• Major North American telecommunications company
• Publicly traded company
• Over $15 billion in revenue and 65,000 employees

Problem

• Text messaging is a target for phishing, fraud and spamming techniques
• Existing detection approach relied on legacy tactics and static rules
• Inability to adapt to new spamming techniques
• Large resource costs from manual review of flagged messages

Solution

• Created an Machine Learning model using the Splunk MLTK
• Used a classification approach for predicting SMS spam
• Observations/events were categorized into discrete groups

Result

• The ML model found to be 93% accurate, significantly reducing false positives
• Greatly reduced cost of internal resources required for manual checks
• Model is continually learning and improving from data in an unsupervised manor
• Removed all need for static rules and thresholds