Kernel Mode Steganography Detection via File Size Discrepancy
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Solution Overview
Problem
Traditional malware detection methods are ineffective in identifying unknown malware, generating false positives and negatives, and failing to detect malware that evades detection by recognizing sandbox environments.
Innovation Solution
The system performs real-time detection and protection from malware and steganography in kernel mode by analyzing file operations, using static and dynamic analysis, and monitoring system state changes to identify unauthorized data encryption and hidden data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signature-based malware detection is used, then known malware can be detected, but unknown malware cannot be identified
Solution Approach 1:
The system performs preliminary actions by creating a baseline state of the system before malware execution and comparing it with the current state to detect unauthorized changes. This preliminary state capture enables detection of both known and unknown malware through state comparison rather than signature matching.
Solution Approach 2:
The system monitors changes in system parameters and state variables to detect malware behavior. By tracking modifications to system state, file operations, registry changes, and process behaviors, the system can identify malware based on parameter changes rather than static signatures, enabling detection of novel threats.
2Reliability
If run-time heuristic scanning is used, then malware behavior can be detected, but many false positives and false negatives are generated
Solution Approach 1:
The system segments malware detection into multiple independent state comparison operations, analyzing different aspects of system state (files, registry, processes, network) separately. This segmentation allows for more precise control over detection thresholds and reduces false positives by focusing on specific state changes rather than broad heuristic rules.
Solution Approach 2:
The system implements feedback mechanisms where detection results and system state information are continuously monitored and used to adjust detection sensitivity. By comparing actual system state changes against expected changes, the system can distinguish between legitimate operations and malware behavior, reducing false positives and negatives.
3Measurement precision
If sandboxing is used to execute suspicious files, then malicious behavior can be observed, but malware that recognizes sandbox environments can evade detection
Solution Approach 1:
Instead of executing suspicious files in a sandbox and observing behavior, the system inverts the approach by monitoring the system state and detecting unauthorized changes caused by malware execution. This reversal allows detection of malware behavior in the actual system environment rather than an artificial sandbox, preventing evasive malware from hiding its true nature.
Solution Approach 2:
The system uses system state changes as an intermediary to detect malware behavior indirectly. Rather than directly observing malware execution in a sandbox, the system monitors the intermediary effects of malware on system state (file modifications, registry changes, process creations), which reveals malicious behavior even when malware attempts to evade direct detection.
4Ease of operation
If static code analysis is used, then malware can be analyzed without execution, but malware cannot be detected reliably
Solution Approach 1:
The system replaces mechanical static code analysis with a state-based detection mechanism that monitors actual system state changes. This substitution moves from analyzing code structure statically to observing the dynamic effects of code execution on system state, significantly improving detection reliability while maintaining the benefit of non-executive analysis through state comparison.
Data Source
AI summary
A method for real-time detection of and protection from steganography in a kernel mode comprises detecting transmission of a file via a firewall, an operating system, or an e-mail system. A size of the file is determined. From a file system, a stored filesize of the file is retrieved. The determined size of the file is compared to the stored filesize of the file. Responsive to the determined size of the file being larger than the stored filesize of the file, steganography detection analytics are executed on the file. Responsive to the steganography detection analytics indicating presence of steganography in the file, a steganography remediation action is executed, and information is transmitted describing the steganography to a client device.