Watermark Cookies for Malware Detection in Virtual Sandboxes
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Solution Overview
Problem
Existing technologies face challenges in effectively detecting and clustering malicious HTTP cookies, which are often used by malware for communication and data exchange.
Innovation Solution
The proposed solution involves a system and process for malicious HTTP cookies detection and clustering, which includes extracting cookies from network traffic, determining their association with malware, and generating signatures based on these cookies. This process involves preprocessing cookies, analyzing strings and patterns, and applying false positive filters to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional firewall rules are used to detect malicious cookies, then the detection process is simple, but the detection precision is low and false positives occur frequently
Solution Approach 1:
The patent segments the cookie detection process into multiple independent modules: extraction module, preprocessing module (normalization, tokenization), feature extraction module, clustering module, and detection module. Each module handles a specific aspect of the detection process, allowing for specialized processing at each stage while maintaining overall system manageability despite the increased complexity.
Solution Approach 2:
The patent applies preliminary actions by preprocessing cookies before analysis - normalizing cookie formats, tokenizing them into components, and extracting features in advance. This preliminary processing prepares the data for more effective clustering and detection, improving precision by ensuring consistent input formats and highlighting relevant characteristics before the actual detection occurs.
2Measurement precision
If comprehensive cookie analysis is performed to improve detection accuracy, then the detection precision improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing cookies through normalization, tokenization, and feature extraction before the actual detection phase. This upfront preparation reduces the computational burden during detection by converting diverse cookie formats into standardized representations and pre-identifying key features, thereby improving accuracy without proportionally increasing total processing time.
Solution Approach 2:
The patent creates simplified copies or representations of cookies through tokenization and feature extraction. Instead of analyzing entire raw cookies, the system works with extracted features and normalized representations that capture essential characteristics while reducing data volume, enabling more efficient processing while maintaining detection accuracy.
3Reliability
If watermark cookies are injected into the virtual environment to detect malware, then the malware detection capability is improved, but the virtual environment complexity increases
Solution Approach 1:
The patent uses watermark cookies as intermediaries between the virtual environment and malware detection. These specially injected cookies act as markers that malware may attempt to access or modify. The watermark cookies themselves are simple data structures, but they enable sophisticated detection by serving as intermediaries that bridge the clean environment and potential malware behavior, improving reliability without significantly complicating the virtual environment setup.
Data Source
AI summary
Techniques for malware detection using watermark cookies are disclosed. In some embodiments, a system, process, and/or computer program product for malware detection using watermark cookies includes receiving a sample at a cloud security service; injecting a watermark cookie in a virtual environment to provide a modified virtual environment; detonating the sample in the modified virtual environment, wherein the modified virtual environment is instrumented for monitoring activities associated with the sample during automated malware analysis of the sample; detecting whether the watermark cookie was accessed in the modified virtual environment during the automated malware analysis of the sample; and determining whether the sample is malware based on whether the watermark cookie was accessed in the modified virtual environment.