PDF Malware Classification to Limit Deep-Scanning Overhead
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Solution Overview
Problem
Existing technologies struggle to detect stealthy malware, such as zero-day malware, in everyday documents like PDF files, as static or signature-based detections fail to identify obfuscated or unknown malware strains, leading to significant processing overhead and potential infection risks.
Innovation Solution
A cloud-based network security system analyzes PDF files using heuristic rules to classify them as benign or suspicious before deep-scanning, extracting features like object, code, and embedded features without opening the document, and routes documents to deep-scanning only if suspicious, thereby reducing the need for extensive deep-scanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static or signature-based detection is used, then processing speed is improved, but malware detection accuracy deteriorates because zero-day and polymorphic malware cannot be detected
Solution Approach 1:
The detection system is segmented into two independent components: a fast static analysis component that extracts features and applies signature-based detection, and a slower deep-scanning component that performs dynamic analysis in a sandbox environment. This segmentation allows the system to process most documents quickly while applying comprehensive analysis only when necessary, thus maintaining both high processing speed and accurate detection of zero-day malware.
Solution Approach 2:
A classification model serves as an intermediary between static detection and deep-scanning. This model analyzes extracted features and predicts the likelihood of malware presence, routing documents to appropriate processing paths. The intermediary enables the system to leverage both fast static analysis and accurate deep-scanning without directly combining their processing methods, resolving the contradiction between speed and accuracy.
2Measurement precision
If deep-scanning is applied to all documents, then malware detection accuracy is improved, but processing overhead and resource consumption increase significantly
Solution Approach 1:
Instead of applying full deep-scanning to all documents, the system applies partial action by using a classification model to identify only suspicious documents that require deep-scanning. This selective approach performs comprehensive analysis (excessive action) only where needed, while using lighter static analysis for the majority of benign documents, thus reducing overall processing overhead while maintaining high detection accuracy for malicious files.
Solution Approach 2:
The system performs preliminary static analysis and classification before committing resources to deep-scanning. By extracting features and applying the classification model in advance, the system identifies suspicious documents that warrant further investigation. This preliminary action filters out benign documents early, preventing unnecessary resource consumption on documents that do not require intensive analysis.
3Productivity
If heuristic rules are applied to classify documents, then the number of documents requiring deep-scanning is reduced, but the complexity of the detection system increases
Solution Approach 1:
The complex detection system is segmented into modular components: feature extraction module, heuristic rule engine, classification model, and routing logic. Each module performs a specific function and can be independently developed, tested, and maintained. This segmentation manages system complexity by organizing numerous heuristic rules and detection mechanisms into discrete, manageable units that work together through well-defined interfaces.
Data Source
AI summary
A cloud-based network security system (NSS) is described. The NSS extracts information about a document (e.g., a portable document format (PDF) file) and uses heuristic rules to analyze the information to predict whether the document contains malicious software. Specifically, prior to detonation of the document, object features, code features, and embedded features of the document are extracted. The extracted information is input to a classification engine that applies sets of heuristic rules to groups of the features of the document to provide an output indicating a prediction of whether the document contains malware. A routing engine provides the document for further analysis (e.g., deep scanning) if the document is suspicious or bypasses the further analysis if the document is benign. Security policies can then be applied based on the classification.