QuickHeal - Malware Prediction using Machine Learning

We helped QuickHeal to build a Machine Learning powered Auto-Malware detector that scans tens of thousands of files/hour to identify and classify threats automatically.

It Saved them hundreds of man-hours per day with improved accuracy of malware detection by more than 70%.

The Challenges

The key challenges were -

  • To gain competitive advantage by accurate prediction of malicious / non-malicious files using Machine learning
  • Model size must be small
  • Desired False Positive Rate (FPR) < 0.5 %
  • Training on massive data (Tens of millions of data points)

Coreview’s Solution

CoreView team worked in an agile manner to deliver a state-of-the-art system.

  • Advanced ML classification algorithm to predict malicious files
  • Detailed feature engineering with a variety of file data
  • Classifiers models like Gradient Boosting, Decision Tree, etc …
  • Automated pipeline for data ingestion and training
  • APIs for consumption, training, feedback of model
  • Leaner model and very fast prediction, minimal compute
  • Deployed with PySpark on AWS

The Results – Transformed Customer Experience

This helped the company to bring a great customer experience with -

  • Consistently achieved desired FPR (0.3 %)
  • The lean model with faster and accurate prediction
  • Saved hundreds of thousands of man-hours annually in manual work
  • Helped the team to utilize more time on new research

Saved hundreds of thousands of man-hours annually in manual work of malicious file detection.

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