Deep Learning Model for Abusive Activity Detection
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Solution Overview
Problem
Existing machine learning models struggle to effectively detect abusive activities in online networks due to the need for separate models for each site surface, reliance on human-engineered features, and inefficiencies in processing sequential data, which limits their ability to detect sophisticated and adversarial attacks.
Innovation Solution
A deep learning machine-learned model that operates directly on raw user activity sequences, using a transition matrix to aggregate and standardize request paths, and a deep convolutional neural network to classify abusive activities, eliminating the need for human-curation and improving detection precision and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for abusive activity detection, then the system can identify basic abusive behaviors, but the model requires separate training for each site surface and relies on hand-engineered features, reducing scalability and detection precision
Solution Approach 1:
The patent implements a universal deep learning model that processes user activity sequences across multiple site surfaces through a standardized interface. The model accepts normalized activity data (clicks, views, scrolls, etc.) regardless of the specific site surface, eliminating the need for separate models for each surface while maintaining high detection precision through learned feature representations
Solution Approach 2:
The patent replaces hand-engineered feature extraction with automatic feature learning through deep neural networks. Instead of manually designing features based on domain knowledge, the system uses embedding layers and convolutional neural networks to automatically learn relevant patterns from raw activity sequences, improving both precision and reducing model complexity
2Reliability
If traditional machine learning models process sequential user activity data, then the system can analyze user behaviors, but the models struggle with sophisticated adversarial attacks and fail to capture temporal patterns effectively
Solution Approach 1:
The patent employs dynamic processing of user activity sequences through convolutional neural networks that can adapt to varying patterns of abuse. The model processes sequences of normalized activities (clicks, views, scrolls, pauses) with learned embeddings that capture temporal relationships, enabling reliable detection that adapts to sophisticated adversarial behaviors rather than relying on static rules
Solution Approach 2:
The system uses self-supervised learning where the model learns to represent user behaviors automatically from raw activity data without manual feature engineering. The embedding layers and neural network components learn relevant patterns independently, making the system inherently more adaptable to new adversarial techniques while maintaining reliability
3Productivity
If hand-engineered features are used for abusive activity detection, then the system can interpret specific abusive behaviors, but the feature engineering process is time-consuming and limits the system's ability to detect novel attack patterns
Solution Approach 1:
The patent replaces the manual feature engineering process with automatic feature extraction through deep learning embeddings. The system normalizes raw activity data (clicks, views, scrolls, pauses) and passes it through embedding layers that automatically learn meaningful representations, eliminating the time-consuming manual feature creation process while improving detection of novel attack patterns
Solution Approach 2:
The system performs self-learning of feature representations through the neural network architecture. The embedding layers and convolutional networks automatically identify relevant patterns in user activity sequences without human intervention, significantly reducing the time required to adapt to new abusive behaviors while maintaining high detection efficiency
Data Source
AI summary
In an example embodiment, a deep learning algorithm is introduced that operates on a transition matrix formed from user activities in an online network. The transition matrix records the frequencies that particular transitions between paths of user activity have occurred (e.g., the user performed a login activity, which has one path in the network, and then performed a profile view action, which has another path in the network). Each transition matrix corresponds to a different user's activities.