Machine Learning Code Word Detection via Vector Space Alignment
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Solution Overview
Problem
Code words used in communication can be difficult to detect due to their non-obvious nature, inconsistent usage, and the complexity of identifying them within large volumes of communication.
Innovation Solution
A computer-implemented method using a machine learning model to identify potential code words by generating vector spaces for word embeddings from target and reference corpuses, performing vector rotation to improve alignment, and identifying word embeddings that are not aligned as potential code words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional human analysis is used to detect code words, then the detection process can be understood and explained, but the detection accuracy and efficiency are insufficient due to the non-obvious nature and inconsistent usage of code words
Solution Approach 1:
The patent replaces manual human analysis with an automated computational system that uses natural language processing and machine learning algorithms. The system automatically processes communications, performs vector space modeling, calculates alignment scores, and identifies potential code words without human intervention, thereby dramatically improving both detection accuracy and efficiency while eliminating the time loss associated with manual analysis
Solution Approach 2:
The patent transforms the code word detection problem into a parameter-based computational task by representing words as vectors in a vector space and calculating alignment scores between corresponding vectors from target and reference corpuses. This parameter transformation allows the system to objectively measure and compare word usage patterns, enabling accurate and efficient automated detection of code words based on quantitative parameters rather than subjective human judgment
2Measurement precision
If the volume of communications to be analyzed is increased, then the detection coverage is improved, but the complexity of identifying code words increases due to the large volume of data
Solution Approach 1:
The patent divides the large volume of communications into two separate corpuses: a target corpus containing the communications to be analyzed and a reference corpus containing normal communications without code words. This segmentation allows the system to process each corpus independently, creating separate vector space models for comparison, thereby managing the complexity of analyzing large data volumes while maintaining comprehensive detection coverage
Solution Approach 2:
The patent introduces vector space modeling as an intermediary representation layer between the raw communication data and the code word identification process. By transforming words into vector representations and using alignment score calculations as an intermediary metric, the system simplifies the complex task of comparing large volumes of communications, making the analysis process more manageable and scalable
3Reliability
If code words are made more secretive to maintain their effectiveness, then their detectability by outsiders is reduced, but the difficulty of detecting them even by analysis systems increases
Solution Approach 1:
The patent uses feedback from the alignment score calculations to iteratively identify and refine the detection of potential code words. The system calculates alignment scores between corresponding word vectors from the target and reference corpuses, uses these scores as feedback to identify words with low alignment as potential code words, and can iteratively improve detection accuracy by analyzing the identified code words and their contexts, thereby maintaining secrecy while improving detectability
Solution Approach 2:
The patent changes the detection parameters from looking for obvious semantic differences to measuring subtle alignment score deviations in vector space representations. This parameter change allows the system to detect highly secretive code words that maintain their normal meanings while showing statistically significant deviations in usage patterns, thereby maintaining code word secrecy while enabling detection through quantitative parameter analysis
Data Source
AI summary
Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.