Random String Detection via Character Transition Analysis
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Solution Overview
Problem
Malicious software often employs randomly generated character strings to evade detection, making it difficult for security systems to identify and prevent malicious activity in computer networks.
Innovation Solution
A computing device is equipped with a method to identify randomly generated character strings by analyzing character transitions within the strings and comparing them to a threshold, using techniques such as n-grams and regular expressions, to determine if a string is randomly generated, facilitating the detection of potentially malicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If malicious software uses randomly generated character strings, then the malware can evade detection by security systems, but this makes it difficult to identify and prevent malicious activity
Solution Approach 1:
The patent transforms the detection problem from analyzing random character strings to analyzing character transition patterns. By changing the parameter from raw string content to transition frequency between character types (alphanumeric, special, etc.), the system can reliably detect randomly generated strings while maintaining manageable analysis complexity.
Solution Approach 2:
The patent applies a metaphorical 'color coding' approach by categorizing characters into different types (alphanumeric, special characters, etc.) and analyzing transitions between these categories. This classification system enables the detection of random string patterns without requiring direct analysis of the random content itself.
2Measurement precision
If security systems analyze every character string in detail, then detection precision improves, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential feature needed for detection - the transition patterns between character types - while discarding the rest of the string content. This extraction approach maintains high identification accuracy for random strings while dramatically reducing processing time and computational resources required.
Solution Approach 2:
Instead of performing complete detailed analysis of every string, the patent applies partial action by analyzing only the character transition patterns. This partial analysis is sufficient to detect random strings with high accuracy while avoiding the time cost of exhaustive examination.
3Reliability
If traditional signature-based detection is used, then known malware can be identified, but newly generated or obfuscated malware strings cannot be detected
Solution Approach 1:
The patent creates a universal detection method that works across different malware types and obfuscation techniques. By detecting character transition patterns rather than specific string content, the system maintains high detection rates for known malware while also being adaptable to newly generated or obfuscated malware strings.
Solution Approach 2:
The patent employs a dynamic detection approach that adapts to various string formats and malware types. The character transition analysis method remains effective whether the malware uses known signatures, randomly generated strings, or obfuscated content, providing versatile detection capability across changing threat landscapes.
Data Source
AI summary
Examples relate to identifying randomly generated character strings. In one example, a computing device may: receive a character string that includes two or more characters; identify a number of character transitions included in the character string, each character transition being a change in character type within an n-gram of the character string, where n is a positive integer; and determine, based on the number of character transitions, whether the character string was randomly generated.