Document Execution Threat Detection With ML-Based Remedial Actions
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Solution Overview
Problem
Document execution environments are vulnerable to malicious threats and activities, necessitating a system to identify and respond to such malicious behavior.
Innovation Solution
A machine learned model is trained on a set of information representing incidents of malicious behavior and remedial actions, enabling it to detect and recommend mitigation strategies within a document execution environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are used to protect documents in an online execution environment, then basic security is provided, but the system cannot effectively identify and respond to sophisticated malicious behavior
Solution Approach 1:
The system employs machine learning models that automatically analyze document execution activity and identify malicious behavior patterns without requiring manual security configuration. The models self-adjust to detect threats based on trained patterns of malicious activity, reducing the need for complex manual security rule management while improving detection reliability
Solution Approach 2:
Traditional mechanical security measures (manual rules, static filters) are replaced with intelligent machine learning systems that dynamically analyze behavior patterns. The patent substitutes automated ML-based detection for manual security configuration, enabling the system to adapt to sophisticated threats without proportionally increasing operational complexity
2Measurement precision
If comprehensive monitoring of document execution activity is implemented to detect malicious behavior, then detection accuracy improves, but system performance and processing speed may deteriorate
Solution Approach 1:
The system applies machine learning models selectively to analyze only the portions of document execution activity that exhibit suspicious patterns. Rather than comprehensively analyzing every single action, the ML models identify and focus on anomalous behavior sequences, achieving high detection accuracy while minimizing the performance overhead associated with monitoring all activity
Solution Approach 2:
The machine learning models continuously learn from and adapt to new malicious behavior patterns while maintaining steady detection performance. The system sustains accurate threat detection over time without requiring increasing computational resources, as the models are designed to maintain consistent processing efficiency despite the ongoing nature of threat evolution
3Reliability
If a machine learning model is trained on extensive malicious behavior data to improve detection capability, then detection reliability improves, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary training of machine learning models using historical malicious behavior data before deployment to production environments. By pre-training models with extensive malicious activity patterns in advance, the system achieves high detection reliability from the start of operational use, avoiding the need for time-consuming training during live document processing
Solution Approach 2:
The patent employs techniques to optimize model training parameters and architecture to reduce training time while maintaining detection reliability. By adjusting learning rates, batch sizes, and model complexity parameters, the system achieves efficient training that balances comprehensive malicious behavior pattern recognition with acceptable training time and computational resource consumption
Data Source
AI summary
A document execution engine that receives a document for execution within a document execution environment. The document execution engine may also detect activity within the document execution environment associated with the received document, and apply the trained machine learned model to the detected activity to determine if the detected activity is representative of malicious behavior and, in response to determining that the detected activity is representative of malicious behavior, to identify remedial actions that can mitigate the malicious behavior. The document execution engine may also provide, to a device of a user, a recommendation to perform the identified remedial actions.