Address Tuple Database for Malicious File Detection
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Solution Overview
Problem
Existing cybersecurity systems face challenges with high false positives and false negatives in identifying malicious files, lack flexibility, and are often limited to specific operating systems and types of attacks, failing to provide comprehensive protection across various environments and file types.
Innovation Solution
A system and method that utilize a learning server with a hardware processor to perform dynamic and static learning, fetching sample files, tracing process branches, and generating tuple addresses to create a database for identifying malicious files, enabling multi-environmental email security and efficient protection against various types of cyber-attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing cybersecurity systems use traditional scanning methods, then they can identify some malicious files, but they produce high false positives and false negatives
Solution Approach 1:
The patent replaces traditional mechanical signature-based scanning with a learning-based system that uses machine learning models to analyze file behavior and characteristics. The learning server performs dynamic learning by executing sample files and tracing process branches, replacing static pattern matching with adaptive behavioral analysis to improve identification accuracy and reduce false positives.
Solution Approach 2:
The system changes the parameters used for file analysis from fixed signatures to dynamic behavioral parameters. By monitoring process branches, memory allocations, and execution patterns during dynamic learning, the system transforms static file identification into a multi-parameter behavioral assessment, improving reliability in detecting malicious files.
2Reliability
If cybersecurity systems are designed for specific operating systems and attack types, then they can provide specialized protection, but they lack flexibility and cannot protect across various environments
Solution Approach 1:
The patent creates a universal cybersecurity system that can protect against multiple types of attacks across different operating systems. The learning server is designed to perform dynamic learning on various file types and formats, adapting to different operating system environments. The system generates platform-specific tuple addresses and can analyze both executable and non-executable files, providing multi-functional protection without requiring separate specialized systems.
Solution Approach 2:
The system transitions from static, OS-specific security configurations to dynamic, adaptive security analysis. The learning server dynamically adjusts its analysis methods based on the file type, operating system environment, and detected behavioral patterns. This dynamic approach allows the same system to effectively protect against diverse attack types across multiple platforms.
3Measurement precision
If cybersecurity systems use comprehensive scanning of all files, then they can detect malicious content, but they consume significant time and resources
Solution Approach 1:
The patent applies partial action by focusing scanning resources on high-risk file types and behaviors identified through dynamic learning. Instead of uniformly scanning all files with the same intensity, the system uses the learned tuple addresses and behavioral patterns to prioritize analysis of suspicious files, reducing overall scanning time while maintaining detection effectiveness for malicious content.
Data Source
AI summary
A system and method generating a database of tuple addresses associated with a computer program, the method comprising fetching from a repository of sample files a sample file suitable for running by the computer program, and performing dynamic learning of the sample file to obtain tuple addresses used by the computer program in loading of the sample file, the dynamic learning comprising while loading of the sample file by the computer program, monitoring loaded processes and modules, for each loaded process, tracing process branches, upon identification of a mispredicted branch, getting an address tuple of the mispredicted branch, and identifying a module to which the tuple belongs based on the module's base address.