Shellcode Detection via Mnemonic Distribution Analysis
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Solution Overview
Problem
Current malware detection systems face challenges in reliably identifying polymorphic shellcode embedded within computer files, as it disguises itself by mutating its structure to avoid detection, making it difficult to distinguish from benign code.
Innovation Solution
The method involves converting binary code into mnemonic code, analyzing the distribution of mnemonics, and using a non-parametric, non-Markovian Bayesian approach with machine-learning models to determine the likelihood of shellcode presence by comparing probabilistic distributions, thereby reducing computational resources and time required for detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If polymorphic shellcode mutates its structure to disguise itself within benign files, then it avoids detection by traditional methods, but it becomes increasingly difficult to reliably identify and distinguish from legitimate code
Solution Approach 1:
The patent transforms the detection approach by changing the parameter space from analyzing raw binary code or static signatures to analyzing the statistical distribution of mnemonics in disassembled code. This parameter transformation enables reliable detection of polymorphic shellcode by focusing on distributional characteristics that remain consistent despite structural mutations.
Solution Approach 2:
The patent replaces traditional mechanical/deterministic detection methods (signature matching, heuristic analysis) with a probabilistic statistical model. By using distribution analysis and probabilistic reasoning, the system can reliably identify shellcode even when its structure continuously mutates, substituting rigid detection mechanics with flexible statistical inference.
2Reliability
If traditional malware detection systems analyze entire computer files for shellcode, then they may detect malicious code, but they consume excessive computational resources and time
Solution Approach 1:
The patent segments the file analysis process into distinct phases: binary-to-mnemonic disassembly, mnemonic distribution extraction, and probabilistic shellcode likelihood assessment. This segmentation allows the system to process files efficiently by breaking down the complex detection task into manageable statistical operations rather than analyzing every byte of the entire file.
Solution Approach 2:
Instead of performing exhaustive analysis of entire files, the patent applies partial action by focusing computational resources on analyzing the distribution patterns of mnemonics. This partial analysis approach achieves sufficient detection accuracy without the excessive computational cost of examining every aspect of the file, thereby improving productivity while maintaining reliability.
3Ease of manufacture
If detection systems use white lists or assume specific code structures to identify shellcode, then they simplify the detection process, but they fail to detect polymorphic variants that do not match known patterns
Solution Approach 1:
The patent creates a universal detection mechanism that does not rely on white lists or specific structural assumptions. The mnemonic distribution analysis approach is multi-functional, capable of detecting various types of shellcode including polymorphic variants, without requiring separate detection rules for each threat type. This universal approach maintains ease of implementation while achieving broad adaptability.
Solution Approach 2:
The patent introduces dynamics into the detection system by using probabilistic models that can adapt to different code patterns without requiring manual updates. The statistical distribution analysis dynamically adjusts to identify shellcode characteristics regardless of their specific structure or mutation state, providing both implementation simplicity and high adaptability to new threats.
Data Source
AI summary
Identifying shellcode in a sequence of instructions by identifying a first instruction, the first instruction identifying a first bound of a sequence of instructions, identifying a second instruction, the second instruction identifying a second bound of the sequence of instructions, and generating a distribution for the sequence of instructions, bounded by the first instruction and the second instructions, the distribution indicative of whether the sequence of instructions is likely to include shellcode.