Password Semantic Analysis Pipeline Using NLP Tokenization
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Solution Overview
Problem
Conventional password validation mechanisms in computing systems are inadequate as they rely on simplistic lexical rules and do not perform semantic analysis, leading to weak passwords being deemed strong, especially when they contain named entities that are easily predictable.
Innovation Solution
A password semantic analysis pipeline using machine learning models with Natural Language Processing (NLP) techniques to analyze password components, determine their significance, and generate a master password score by chunking passwords into tokens, applying NLP methodologies, and using Levenshtein distance to assess proximity to commonly used passwords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional lexical rules are used for password validation, then the validation process is simple and fast, but the accuracy of password strength assessment deteriorates
Solution Approach 1:
The password validation process is segmented into multiple stages: initial lexical rule filtering followed by deep semantic analysis using NLP models. The password is divided into tokens that are individually analyzed for named entities, syntax patterns, and semantic relationships, allowing both quick rejection of obviously weak passwords and thorough evaluation of potentially strong ones.
Solution Approach 2:
Natural Language Processing models serve as an intermediary between simple lexical rules and final password strength determination. The NLP model analyzes semantic relationships, named entities, and contextual patterns that lexical rules miss, acting as a bridge that enhances assessment accuracy without completely replacing the efficiency of rule-based filtering.
2Measurement precision
If semantic analysis with NLP is applied to password validation, then the accuracy of identifying weak passwords improves, but the computational complexity increases
Solution Approach 1:
Lexical rules and basic patterns are applied as preliminary actions before invoking complex NLP models. This preliminary filtering eliminates obviously weak passwords without requiring full semantic analysis, reducing the overall computational burden while maintaining high accuracy for passwords that pass the initial filter.
Solution Approach 2:
Different analysis methods are applied to different parts of the password based on their characteristics. Named entities, dates, and specific patterns trigger targeted NLP analysis, while other portions use lighter-weight lexical rules. This localized approach applies computational resources where they are most needed rather than uniformly across the entire password.
3Reliability
If comprehensive NLP analysis is performed on each password token, then the detection of named entities and patterns improves, but the processing time increases
Solution Approach 1:
Basic tokenization and lexical analysis are performed as preliminary actions before applying comprehensive NLP methods. This initial processing identifies obvious patterns and structures, allowing the system to skip or reduce NLP analysis for passwords that clearly fail basic criteria, thus reducing overall processing time while maintaining detection accuracy for suspicious patterns.
Data Source
AI summary
Disclosed herein are methods, systems, processes, and machine learning paradigms to implement a password semantic analysis pipeline. A password semantic analysis pipeline model is trained according to one or more machine learning techniques to at least (a) determine, based on given characteristics data of a given network environment, whether each of several tokens that are chunked portions of a data structure input as a password in an application is a known syntax type or a recognized entity, (b) generate, using the password semantic analysis pipeline model, a password strength score that is a combination of a confidence score determined for each of the plurality of tokens and a weight factor assigned to the known syntax type or the recognized entity, (c) apply the password strength score to the data structure input as the password in the application, and (d) provide an output to the application indicating whether the data structure input as the password is acceptable or unacceptable for continued access to the application.