Watermark Cookies for Malware Detection in Virtual Environments
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Solution Overview
Problem
Current technologies lack effective methods for detecting and clustering malicious HTTP cookies, which are used by malware to facilitate unauthorized access and data transmission, leading to difficulties in identifying and preventing malware propagation within networks.
Innovation Solution
A system and process for malicious HTTP cookies detection and clustering, involving the extraction of cookies from network traffic, analysis of patterns, and generation of signatures to identify malicious cookies, along with the use of watermark cookies in a virtual environment to detect malware by monitoring access to these cookies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional firewall rules are used to filter network traffic, then basic unauthorized access protection is provided, but malicious HTTP cookies cannot be effectively detected or clustered
Solution Approach 1:
The patent segments the cookie analysis process into distinct components: extraction of cookies from HTTP traffic, pattern matching against known malicious signatures, clustering similar cookies together, and hierarchical organization. This segmentation transforms the complex problem of malicious cookie detection into manageable stages, improving detection reliability while controlling analytical complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-establishing clusters of malicious cookie patterns and storing them as signatures before actual detection occurs. When network traffic is analyzed, cookies are compared against these pre-computed signatures, significantly improving detection speed and accuracy without requiring complex real-time analysis of every cookie.
2Measurement precision
If comprehensive cookie analysis is performed to identify malicious patterns, then malware detection accuracy improves, but processing time and system resources increase
Solution Approach 1:
The patent pre-computes and stores cookie patterns and their hierarchical relationships before actual traffic analysis. By organizing cookies into clusters and pre-establishing signatures, the system avoids performing comprehensive analysis on every cookie during traffic monitoring, thus maintaining high identification accuracy while reducing processing time.
Solution Approach 2:
The patent creates simplified representations (signatures) of malicious cookie patterns that capture essential characteristics without requiring full cookie analysis. These signature copies enable rapid comparison and identification during traffic analysis, maintaining detection precision while significantly reducing computational overhead.
3Object-affected harmful factors
If malware samples are detonated in a clean virtual environment, then safe analysis is enabled, but detection of cookie-stealing malware requires additional watermark cookies
Solution Approach 1:
The patent pre-installs watermark cookies into the virtual environment before detonating malware samples. This preliminary setup enables automatic detection of cookie-stealing behavior without requiring complex analysis tools or post-execution forensic investigation, maintaining safety while simplifying the detection mechanism.
Solution Approach 2:
The watermark cookies act as intermediaries between the virtual environment and the malware. By embedding detectable markers (watermark cookies) in the environment, the system enables indirect detection of malicious behavior through cookie access patterns, simplifying the overall detection architecture while maintaining safety.
Data Source
AI summary
Techniques for cookies watermarking in malware analysis are disclosed. In some embodiments, a system, process, and/or computer program product for cookies watermarking in malware analysis includes receiving a sample at a cloud security service; detonating the sample in an instrumented virtual environment; and determining that the sample is malware based on detecting an attempt to access a watermark cookie during an automated malware analysis using the instrumented virtual environment.