Machine Learning Detection of Obfuscated Office Macros and OLE Objects
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Solution Overview
Problem
Existing malware detection systems rely on signature-based methods that are ineffective against new or zero-day attacks, particularly those using obfuscated macros and OLE objects in Microsoft Office documents, which are increasingly used to deliver malicious payloads.
Innovation Solution
A system utilizing machine learning and feature engineering to detect malicious macros and OLE objects in Microsoft Office documents without relying on known signatures, by training a supervised machine learning model with features derived from obfuscation techniques used by malware authors, such as Random, Split, Encoding, and Logic obfuscation, to classify documents as safe, suspicious, or malicious.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection methods are used, then detection reliability is maintained for known malware, but detection capability fails against new or zero-day attacks with obfuscated macros
Solution Approach 1:
The patent transforms the detection approach from signature-based (static parameter matching) to behavior-based (dynamic parameter analysis). It extracts multiple features from macro code including obfuscation techniques, API calls, control flow patterns, and data manipulation behaviors. These parameters are fed into machine learning models that adapt to new threat patterns while maintaining reliability through comprehensive feature analysis.
Solution Approach 2:
The patent replaces the mechanical signature-matching system with an intelligent machine learning-based detection system. Supervised learning models (Random Forest, SVM, Neural Networks) and unsupervised learning approaches automatically learn detection patterns from training data, substituting rigid rule-based mechanics with adaptive intelligent systems that can generalize to new threats.
2Measurement precision
If machine learning models with multiple features are used, then detection accuracy improves to 98.9%, but system complexity increases
Solution Approach 1:
The patent segments the complex detection task into distinct feature extraction modules, each handling specific aspects of macro analysis (obfuscation detection, API call analysis, control flow examination). This modular segmentation allows the system to achieve high accuracy through comprehensive analysis while managing complexity through organized, reusable components.
Solution Approach 2:
The patent creates a universal detection framework that handles multiple document types (Word, Excel, PowerPoint) and various obfuscation techniques through a single multi-functional system. The feature extraction and machine learning components are designed to work across different scenarios, reducing overall system complexity despite the comprehensive detection capabilities.
3Reliability
If comprehensive feature engineering is performed to detect obfuscated threats, then detection effectiveness increases, but processing time and latency increase
Solution Approach 1:
The patent performs preliminary feature extraction and analysis on macro code before final detection decisions are made. By pre-computing features such as obfuscation metrics, API call patterns, and control flow characteristics during document upload or preview stages, the system reduces processing latency during actual security validation while maintaining comprehensive detection effectiveness.
Solution Approach 2:
The patent implements a multi-stage detection process where obviously malicious or obviously safe documents are quickly identified and processed through simplified paths. The machine learning models are optimized to make rapid predictions on high-risk features, allowing the system to rush through critical detection points while still performing comprehensive analysis when needed, thereby reducing overall processing time.
Data Source
AI summary
The technology disclosed relates to cybersecurity attacks and cloud-based security, and specifically to the detection of malicious code that is obfuscated within document files. Such malicious code can be delivered in the form of malicious macros and/or malicious OLE objects stored within document files. The technology disclosed detects obfuscated and malicious code using a trained machine learning model to predict which documents include malicious code, despite such malicious not having a known signature. The technology disclosed can thus predict which documents include malicious code lacking a known signature. Safe documents are allowed into the network. Suspicious documents are subjected to additional processing, including quarantining or sandboxing methods. Malicious documents are rejected from the network. In a further aspect, the disclosed technology combines machine learning with other network security methods, to further increase the capability of a network security system to detect malicious macros and malicious OLE files.