Semantic Code Detection for Intent-Based Malware Flagging
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Solution Overview
Problem
Existing cybersecurity measures struggle to keep up with the exponential increase in cyber threats due to evolving attack vectors, leading to a decrease in security for systems and data, especially in cloud environments, as humans and reactive tools are unable to effectively identify nefarious code that does not conform to traditional malware signatures.
Innovation Solution
A system utilizing natural-language translation of code followed by a machine-learning model to generate a confidence score on the malicious intent of the translated output, flagging code as potentially malicious if the score exceeds a threshold, leveraging techniques such as neural machine translation, Parsing Expression Grammar (PEG) Parser, binary classification, semantic language comparison, and natural-language generation to identify intent and intent-based malicious behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional malware detection methods are used, then detection of known malware signatures is effective, but detection of evolving nefarious code is insufficient
Solution Approach 1:
The patent replaces traditional mechanical signature-matching detection systems with a semantic understanding system that uses natural language translation and machine learning models. This substitution enables the system to comprehend the intent and functionality of code rather than merely comparing it against known signatures, thereby achieving both high reliability for known threats and adaptability to evolving nefarious code through semantic analysis capabilities
Solution Approach 2:
The patent changes the detection parameter from binary signature matching to a continuous confidence score generated by machine learning models. This parameter change allows the system to evaluate code based on semantic understanding of its intent and behavior, enabling it to detect evolving threats that don't match known signatures while maintaining reliable detection of known malware through threshold-based decision making
2Adaptability or versatility
If natural-language translation and machine-learning models are used, then detection of intent-based malicious behavior improves, but system complexity increases
Solution Approach 1:
The patent introduces natural language translation as an intermediary layer between the code and the machine learning model. This intermediary translates complex code into semantic representations that are easier for the model to process, thereby improving intent-based detection while managing system complexity through modular architecture where each component (translator, model, threshold evaluator) performs a specific function
Solution Approach 2:
The patent segments the detection system into distinct modular components: a natural language translation module that converts code to semantic representations, a machine learning model that generates confidence scores, and a threshold evaluation module that makes detection decisions. This segmentation allows each component to be optimized independently and facilitates easier maintenance and updates, managing overall system complexity
3Adaptability or versatility
If semantic understanding is implemented, then detection of zero-day threats improves, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on extensive code datasets and pre-compiling natural language translation rules before actual threat detection. This preliminary preparation enables the system to rapidly process and analyze nefarious code during operation, reducing processing time while maintaining the ability to detect zero-day threats through pre-established semantic understanding capabilities
Data Source
AI summary
The disclosed technology is generally directed to nefarious code detection using semantic understanding. In one example of the technology, a natural-language output is generated by performing a natural-language translation of code. A machine-learning model is used to generate a confidence score that indicates a confidence that the natural-language output is potentially malicious. Based at least in part on whether the confidence score exceeds a first threshold, whether the natural-language output is potentially malicious is determined. Responsive to determining that the natural-language output potentially malicious, the code is flagged as potentially malicious.