ANOMALY DETECTION mMould
OVERVIEW
Deployed Isolation Forest, SVM, and Autoencoders to detect and mitigate fraudulent activities, specifically bot-generated behaviors, thereby enhancing security and integrity in its gaming platform.
Key Highlights
Challenges Addressed
mMould faced challenges such as detecting fraudulent bot activities, adapting to evolving cheating tactics, ensuring scalability amidst growing user bases, and maintaining efficient anomaly detection to prevent disruptions.
Approach Implemented
The solution involved data collection and preprocessing of user interaction data, training models like Isolation Forest for outlier detection, SVM for classification, and Autoencoders for anomaly detection. A hybrid approach combined these models to bolster accuracy and deployed them in a scalable infrastructure for real-time monitoring.
Results Achieved
Implementing the advanced anomaly detection model led to enhanced security with robust fraud detection, dynamic adaptation to new cheating methods, scalability to manage increasing data volumes, and efficient system performance without impacting user experience.
Technology Used
- Isolation Forest for outlier detection.
- Support Vector Machine (SVM) for classification.
- Autoencoders for anomaly detection in user behavior.
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