Tetra Code Malware Detection for Encrypted Zero-Day Classification
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Solution Overview
Problem
Existing malware detection techniques require the presence of malware files and rely on signature-based methods, which are ineffective for zero-day threats and compromise end-to-end encryption, lacking the ability to detect malware families and variants without file access.
Innovation Solution
A system utilizing Tetra code, an encoded presentation of PE files based on structure, enables zero-day detection of malware families and variants by converting string samples to Tetra code, applying homomorphic encryption, and using machine learning to classify encrypted features without accessing the files, allowing for improved network and computer performance with enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based methods are used to detect malware, then detection accuracy for known malware is improved, but the ability to detect zero-day threats deteriorates
Solution Approach 1:
The patent creates cryptographic copies (Tetra codes) of malware files that preserve structural characteristics without requiring the original malware. These Tetra codes are generated using homomorphic encryption, allowing the system to analyze and compare malware structures while maintaining security. The copying approach enables detection of zero-day threats by comparing structural patterns against known malware families without needing actual malware samples.
Solution Approach 2:
The patent transforms the detection approach by changing from content-based parameters to structure-based parameters. Instead of analyzing malware file contents directly, the system converts files to Tetra codes that represent structural characteristics. This parameter transformation allows detection of malware families and variants through structural similarity while preserving encryption and enabling zero-day detection.
2Reliability
If malware files are accessed for analysis, then detection capabilities are improved, but end-to-end encryption is compromised
Solution Approach 1:
The patent introduces Tetra codes as an intermediary between the original malware files and the analysis system. These cryptographic representations serve as mediators that allow structural analysis without exposing the actual file contents. The homomorphic encryption property enables computations on encrypted data, so the intermediary Tetra codes preserve encryption while enabling detection capabilities.
Solution Approach 2:
The patent replaces the mechanical system of direct file access and decryption with a cryptographic substitution approach. Instead of mechanically accessing and analyzing file contents, the system uses homomorphic encryption to perform operations on encrypted representations. This substitution maintains security while enabling analysis through mathematical operations on ciphertexts.
3Measurement precision
If traditional hashing techniques are used for file comparison, then file identification is improved, but the ability to detect malware families and variants deteriorates
Solution Approach 1:
The patent segments the file analysis process into multiple hierarchical levels: individual file hashing, Tetra code generation, family mask creation, and variant identification. This segmentation allows the system to operate at different granularities - from individual file identification to family-level pattern recognition. The segmented approach enables detection of both exact matches and familial relationships through progressively abstracted representations.
Solution Approach 2:
The patent adds a new dimension to file comparison by introducing structural encoding through Tetra codes. Instead of relying solely on traditional hashing in one dimension, the system creates a multi-dimensional representation where files are transformed into cryptographic structures that preserve familial relationships. This dimensional transformation enables detection of variants that share structural characteristics but differ in content.
Data Source
AI summary
A string sample is received from a file in real-time and the string sample is converted to a Tetra code and used to search a database of Tetra code samples, organized by family and then by variant. Responsive to the real-time Tetra code not matching any stored Tetra codes, (a) an internal structure of the Tetra Code is generated to expose correlations of encrypted features of the file, without any access to the file, (b) machine learning is utilized to classify the internal structure of encrypted features against training data of encrypted features, and (c) a label is predicted based on the classification. The real-time Tetra code is stored in the database associated with the new family label and/or the new variant label. Any label for the file string sample is output for potential security actions.