Automated Script Obfuscation Detection via Symbol Frequency Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The widespread use of scripting runtimes in operating systems, such as Powershell, makes it challenging for analysts to identify malicious scripts amidst tens of millions of scripts executed monthly, as these scripts can be a common avenue for introducing malware due to their broad usage and limited anti-virus coverage.
Innovation Solution
An automated system that uses a processor to receive and analyze software programming scripts, identify symbols, determine symbol frequencies, and employ an obfuscation machine learning model to classify scripts as obfuscated, triggering alerts for administrative users to recommend security analysis based on binary obfuscation classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated script analysis is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The automated script analysis system is divided into distinct functional modules: a script reception module that receives scripts, a symbol identification module that identifies symbols in script text, a frequency determination module that calculates symbol frequencies, a normalization module that computes normal scores, and a machine learning classification module that performs obfuscation detection. This segmentation allows each module to perform a specific task efficiently, improving overall productivity while managing complexity through modular design.
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a bridge between raw script data and security analysis results. The model receives normalized symbol frequency data and produces obfuscation probability predictions, serving as an intermediary processing layer that enhances analysis capability without requiring complex custom algorithms throughout the entire system.
2Measurement precision
If manual script analysis is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system implements self-service through automated machine learning classification that performs obfuscation detection without human intervention. The classifier automatically processes scripts, evaluates symbol frequency patterns, and generates detection results, eliminating the need for manual analysis while maintaining consistent precision across large volumes of scripts.
Solution Approach 2:
The system transforms the analysis approach by changing parameters from manual inspection to automated statistical evaluation. Symbol frequencies are calculated and normalized, then fed into a machine learning model that uses probabilistic thresholds to determine obfuscation likelihood. This parameter-based automated approach maintains detection precision while dramatically reducing analysis time.
3Measurement precision
If symbol frequency analysis is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system extracts only the essential feature - symbol frequency information - from the complete script data. By focusing analysis on symbol frequencies rather than examining every aspect of script behavior or structure, the system achieves effective obfuscation detection with reduced computational energy requirements compared to comprehensive analysis approaches.
Data Source
AI summary
Systems and methods enable automated and scalable obfuscation detection in programming scripts, including processing devices that receive software programming scripts and a symbol set. The processing devices determine a frequency of each symbol and an average frequency of the symbols in the script text. The processing devices determine a normal score of each symbol based on the frequency of each symbol and the average frequency to create a symbol feature for each symbol including the normal score. The processing devices utilize an obfuscation machine learning model including a classifier for binary obfuscation classification to detect obfuscation in the script based on the symbol features. The processing devices cause to display an alert indicting an obfuscated software programming script on a screen of a computing device associated with an administrative user to recommend security analysis of the software programming script based on the binary obfuscation classification.