Incremental Machine Learning for Real-Time Anomaly Detection
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Solution Overview
Problem
Existing technologies face challenges in detecting and preventing anomalous behavior, such as fraudulent and malicious activities, on online platforms in real-time, as conventional approaches often rely on post-transaction verification and lack data-oriented proactive detection methods.
Innovation Solution
The implementation of an incremental machine learning framework that continuously updates and adapts machine learning models to detect evolving types of anomalous behavior in real-time, using data from user interactions on online platforms to select and utilize the most effective models for anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional post-transaction verification methods are used, then system complexity is reduced, but detection precision and response time deteriorate
Solution Approach 1:
The system performs preliminary anomaly detection during transaction processing rather than after completion. Machine learning models continuously analyze user behavior patterns in real-time, enabling proactive identification of fraudulent activities before they cause harm, thus improving detection precision while maintaining manageable system complexity through event-driven architecture
Solution Approach 2:
The patent replaces conventional mechanical verification systems with data-oriented machine learning models. These models process user interaction data, device information, and transaction patterns to automatically detect anomalies, substituting manual or rule-based verification with intelligent automated detection that achieves higher accuracy without proportionally increasing complexity
2Speed
If real-time data processing is implemented, then detection speed improves, but energy consumption increases
Solution Approach 1:
The system applies partial processing by focusing computational resources only on transactions that exhibit anomalous characteristics. Machine learning models identify suspicious patterns and trigger detailed analysis only for those cases, rather than processing all transactions at full depth, thus achieving fast detection speed for critical cases while reducing overall energy consumption
Solution Approach 2:
The system implements periodic model updates and retraining cycles rather than continuous full-scale processing. Machine learning models are updated at intervals based on accumulating data, enabling real-time detection during operation while reducing computational energy consumption during model maintenance phases through scheduled batch processing
3Adaptability or versatility
If multiple machine learning models are maintained, then detection versatility improves, but device complexity increases
Solution Approach 1:
The system employs a ensemble of machine learning models where each model is designed to detect specific types of anomalies (fraudulent transactions, account takeovers, bot activities). By maintaining specialized models for different threat categories rather than one generic model, the system achieves comprehensive anomaly type coverage while managing complexity through modular model architecture and centralized model registry
4Adaptability or versatility
If continuous model updating is performed, then adaptability to evolving threats improves, but processing time increases
Solution Approach 1:
The system implements continuous learning through incremental model updates that process new data in streams rather than requiring complete retraining. Machine learning models continuously adapt to evolving threats by incorporating recent transaction patterns and anomaly examples, maintaining up-to-date detection capabilities without significant processing time losses through online learning algorithms
Solution Approach 2:
The system performs preliminary validation and preprocessing of incoming data before model updates. By preparing training data in advance and using techniques like incremental learning and transfer learning, the system reduces the actual update time while maintaining adaptability to new threat patterns, as the heavy lifting of data preparation occurs continuously in the background
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for detection of anomalous behavior on online platforms using machine learning techniques are provided herein. An example method includes obtaining a set of machine learning models configured to detect anomalous behavior associated with users interacting with an online platform and performing an incremental machine learning process on one or more of the machine learning models in the set. The incremental machine learning process may include obtaining data related to interactions of users with the online platform, updating at least one of the machine learning models in the set based on the obtained data, comparing the machine learning models, and selecting one of the machine learning models from the set to be used by the online platform based on the comparison. The method may further include determining, utilizing the selected machine learning model, that a given user is exhibiting anomalous behavior on the online platform.