PDF Network Content Analysis Using Virtual Machine Verification
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Solution Overview
Problem
Existing network security systems are inefficient in detecting malicious PDF network content, as they often fail to identify suspicious characteristics before they cause damage, and current methods are computationally intensive and not scalable for large networks.
Innovation Solution
A system that examines received PDF network content for suspicious characteristics and uses virtual machines to verify the presence of malicious content, employing heuristic analysis and a virtual machine pool to simulate the receipt and processing of network content, allowing for real-time detection and prevention of malicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional antivirus scanning software is used to detect malicious content, then detection capability is provided, but detection occurs after damage has already occurred and the system is not scalable for large networks
Solution Approach 1:
The system performs preliminary analysis of PDF content by extracting and examining metadata, object information, and embedded objects before the content reaches client devices. This preliminary action enables detection of malicious characteristics in advance, preventing damage before it occurs rather than detecting after damage has happened.
Solution Approach 2:
The system extracts specific elements from PDF content for separate analysis, including metadata, object information, embedded objects, and JavaScript code. By taking out these components for individual examination, the system can identify malicious characteristics without processing the entire PDF, enabling faster and more scalable detection.
2Measurement precision
If comprehensive analysis of PDF content is performed, then detection accuracy is improved, but computational resources are consumed and scalability is reduced
Solution Approach 1:
The system segments PDF analysis into multiple independent components: metadata examination, object information analysis, embedded object detection, and JavaScript code examination. Each component can be processed separately and in parallel, enabling comprehensive analysis without consuming excessive computational resources, thus maintaining scalability.
Solution Approach 2:
The system applies partial analysis by examining only the most critical and suspicious elements of PDF content (metadata, object information, embedded objects, JavaScript) rather than processing every possible detail. This partial action approach maintains sufficient detection accuracy while significantly reducing computational overhead and improving scalability.
3Loss of energy
If manual or scheduled scanning is used, then system resources are conserved, but security protection is insufficient and damage can occur between scans
Solution Approach 1:
The system provides continuous security protection by continuously monitoring and analyzing PDF content as it traverses the network, rather than relying on manual or scheduled scans. This continuous action ensures that malicious content is detected and blocked in real-time, eliminating security gaps between scans while maintaining efficient resource consumption through targeted analysis.
Solution Approach 2:
The system automatically analyzes PDF content and makes detection decisions without requiring manual intervention. The automated examination of metadata, objects, and embedded content provides continuous security protection while conserving system resources by eliminating the need for manual scanning operations.
4Productivity
If PDF content is allowed to reach client devices, then network performance is maintained, but malicious content can cause damage and spread
Solution Approach 1:
The system acts as an intermediary between PDF content sources and client devices, examining and analyzing content before it reaches users. This intermediary position enables the system to block malicious content while allowing legitimate content to pass through, maintaining network performance while preventing damage and spread of malicious PDFs.
Data Source
AI summary
Systems and methods for analyzing malicious PDF network content are provided herein. According to some embodiments, a PDF parser examines a body portion of a PDF document received over a network and intended for a digital device and determines if one or more suspicious characteristics indicative of malicious network content are included in the examined body portion of the PDF document. The examined body portion of the PDF document is lesser in size than an entirety of the body portion of the PDF document. When the portion of the body section of the PDF document is determined to include one or more suspicious characteristics indicative of malicious network content, the PDF document is provided to one or more virtual machines associated with the digital device to verify the inclusion of malicious network content in the portion of the body section of the PDF document. Such verification comprises execution of a PDF reader application by the one or more virtual machines to process the portion of the body section of the PDF document and monitor behavior of the PDF document so as to determine if the portion of the body section of the PDF document includes malicious network content.