Obfuscated Content Detection via Machine Learning
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Solution Overview
Problem
Existing methods for detecting obfuscated malicious content are inefficient, as they either rely on costly and time-consuming human review or are prone to missing obfuscated content that bypasses filters.
Innovation Solution
A machine learning model is trained using obfuscated training images labeled with policy violations or approvals, allowing it to predict whether incoming content violates a host's content policy, thereby automating the detection of obfuscated malicious content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If filters are used to reject malicious content, then content security is improved, but obfuscated content can bypass filters and detection accuracy deteriorates
Solution Approach 1:
The patent changes the detection parameters from traditional filter-based binary classification to a probability-based detection system using machine learning models. The model outputs a probability score indicating the likelihood of content being malicious, allowing for more nuanced detection that can handle obfuscated content by learning patterns from training data rather than relying on fixed filter rules.
Solution Approach 2:
The patent replaces the mechanical filter system with a machine learning-based detection system. Instead of using static filters that can be bypassed, the system uses trained neural networks that can adapt to and detect obfuscated content patterns, substituting the mechanical filtering approach with an intelligent detection mechanism.
2Measurement precision
If human review is used to detect obfuscated content, then detection accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent implements a self-service detection system where the machine learning model automatically detects and classifies content without requiring human intervention. The model trains on labeled data and autonomously performs detection tasks, eliminating the need for manual review while maintaining high detection accuracy for obfuscated content.
Solution Approach 2:
The patent creates a digital copy of human detection capability through machine learning models. The model learns from training data that includes labeled examples of malicious and benign content, replicating the detection accuracy of human reviewers but without the time and cost constraints of actual human review.
3Reliability
If machine learning models are trained on obfuscated content, then detection capability is improved, but training complexity and data requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on large datasets of labeled obfuscated content before deployment. This pre-training phase creates the detection capability in advance, allowing the model to handle obfuscated content effectively when it is actually needed, without increasing operational complexity during content review.
Solution Approach 2:
The patent segments the training process into distinct phases: data collection, labeling, model training, and validation. This segmentation allows for manageable training complexity by breaking down the overall task into smaller, more manageable steps, each with specific objectives and deliverables.
Data Source
AI summary
The technology is generally directed to the training and execution of a model to identify policy violating content that has been obfuscated. The model may be trained using obfuscated training images. The obfuscated training images may be associated with one or more labels, such as a policy, obfuscation label, etc. The obfuscated training images and associated labels may be input into the model. During training, the output of the model may be a policy prediction as to whether the obfuscated input images violate the content policy of a host or are approved content for publishing. During implementation, the model may receive content as input and provide as output a policy prediction for the content. The host may use the policy prediction provided by the model to determine whether or not to publish the content.