Malicious Script Detection via Multi-Buffer Segmentation
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Solution Overview
Problem
Existing methods for detecting malicious scripts are inefficient due to the need for significant computing resources and the challenge of obfuscated source code, making it difficult to effectively evaluate scripts for malicious content.
Innovation Solution
An endpoint system processes a target file by normalizing its content, translating tokens into symbols, extracting syntactical structure, and comparing these against heuristic rules and signatures, while utilizing a machine learning model to detect malicious scripts, enabling rapid and resource-efficient detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scripts are evaluated to detect malicious code, then detection accuracy is improved, but computing resources consumed increase significantly
Solution Approach 1:
The script evaluation process is segmented into multiple independent buffers (normalized buffer, tokenized buffer, string buffer, structure buffer), each handling a specific aspect of script analysis. This segmentation allows parallel processing and reduces the computational burden on any single component while maintaining comprehensive detection capability.
Solution Approach 2:
Multiple intermediate buffers are introduced between the script input and the final detection decision. These buffers (normalized buffer, tokenized buffer, string buffer, structure buffer) serve as intermediaries that preprocess and transform the script data into various representations, enabling more efficient analysis and reducing direct computational complexity.
2Object-affected harmful factors
If source code is obfuscated to hide intentions, then script vulnerability to malicious purposes increases, but detection difficulty increases
Solution Approach 1:
Instead of trying to read and understand obfuscated source code directly, the system inverts the approach by transforming the obfuscated code into multiple standardized representations (normalized buffer, tokenized buffer, string buffer, structure buffer). This inversion process strips away obfuscation layers and presents the script's essential characteristics in a standardized format that is easier to detect and analyze.
Solution Approach 2:
The system changes the parameters of the script representation by transforming it through multiple buffers that convert the original obfuscated code into different formats and representations. This parameter transformation makes the script's malicious intentions visible regardless of obfuscation techniques used, as each buffer reveals different aspects of the script's structure and behavior.
3Measurement precision
If script evaluation takes longer, then detection thoroughness is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing the script into multiple standardized buffers (normalized buffer, tokenized buffer, string buffer, structure buffer) before the actual detection analysis. This preliminary transformation prepares the data in advance, allowing the detection process to proceed more quickly and efficiently while maintaining thoroughness, as the heavy lifting of data preparation is done beforehand.
Solution Approach 2:
By segmenting the evaluation process into separate buffer operations that can be performed in parallel or in a streamlined sequence, the system maintains detection thoroughness while reducing overall evaluation time. Each buffer handles a specific transformation task that contributes to comprehensive analysis but can be executed efficiently without significant delays.
Data Source
AI summary
An endpoint system receives a target file for evaluation for malicious scripts. The original content of the target file is normalized and stored in a normalized buffer. Tokens in the normalized buffer are translated to symbols, which are stored in a tokenized buffer. Strings in the normalized buffer are stored in a string buffer. Tokens that are indicative of syntactical structure of the normalized content are extracted from the normalized buffer and stored in a structure buffer. The content of the tokenized buffer and counts of tokens represented as symbols in the tokenized buffer are compared against heuristic rules indicative of malicious scripts. The contents of the tokenized buffer and string buffer are compared against signatures of malicious scripts. The contents of the tokenized buffer, string buffer, and structure buffer are input to a machine learning model that has been trained to detect malicious scripts.