Machine Learning Detection of Malicious Office Documents
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Solution Overview
Problem
The high volume and ease of creation of malicious Microsoft Office and Office Open Extensible Markup Language (XML) documents pose a significant challenge in quickly and accurately detecting malicious macros and embedded objects, which are often exploited for malware distribution.
Innovation Solution
A machine learning-based system that monitors and analyzes Office documents to determine the distribution of malicious macros and embedded objects, selects relevant features, and trains a model to differentiate between benign and malicious content, with a focus on weighing VBA macros and embedded objects based on production traffic insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security methods are used to detect malicious Office documents, then the detection process is simple to implement, but the detection accuracy and speed are insufficient due to the high volume and variety of malicious documents
Solution Approach 1:
The patent introduces machine learning models as an intermediary between traditional security methods and malicious document detection. The ML model analyzes document features (macros, embedded objects, metadata) and provides risk scores, enabling more accurate detection without completely replacing existing security infrastructure. This intermediary approach balances improved accuracy with manageable system complexity.
Solution Approach 2:
The detection system is segmented into multiple components: feature extraction modules that identify specific document characteristics, machine learning models that analyze patterns, and decision-making layers that determine maliciousness. This segmentation allows the system to handle the complexity of detecting various malicious techniques while maintaining modularity and manageability.
2Measurement precision
If comprehensive analysis of all Office document features is performed, then detection accuracy improves, but processing time and computational resources increase significantly
Solution Approach 1:
The system performs partial analysis by focusing on the most indicative features of malicious documents, such as VBA macros and suspicious embedded objects. Rather than analyzing every possible document feature in equal detail, the ML model prioritizes high-risk features based on training data, achieving high detection accuracy while reducing processing time for benign documents that lack these critical features.
3Adaptability or versatility
If the machine learning model is trained on all possible malicious document types, then detection coverage is maximized, but the training process becomes increasingly complex and difficult to maintain
Solution Approach 1:
The machine learning model is designed to be dynamic and adaptable rather than static. The system can be retrained with new malicious document types as they emerge, and the model automatically adjusts to new patterns. This dynamic approach allows the system to maintain high detection coverage across diverse malicious techniques without requiring complete redesign of the training process for each new threat.
4Adaptability or versatility
If traditional signature-based detection is used, then the system is easy to maintain, but it cannot detect new or variant malicious documents that have not been previously identified
Solution Approach 1:
The patent replaces the mechanical, rule-based signature detection system with a machine learning-based approach. Instead of relying on predefined signatures that must be manually updated, the ML model learns patterns from training data and automatically generalizes to detect new malicious variants. This substitution improves adaptability to new threats while the automated nature of ML model updates reduces long-term maintenance burden compared to manual signature database management.
Data Source
AI summary
Systems and methods include, based on monitoring of content including Office documents, determining distribution of malicious Office documents between documents having malicious macros and documents having malicious embedded objects; determining features for the documents having malicious macros and for the documents having malicious embedded objects; selecting training data for a machine learning model based on the distribution and the features; and training the machine learning model with the selected training data.