NLP Cyber Threat Classification System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cyber-security systems face challenges in real-time threat detection and mitigation due to the need for human analysis, which is time-consuming and often incomplete, reducing the ability to react swiftly to cyber-attacks.
Innovation Solution
A method and system for machine-based cyber-threat classification using natural language processing (NLP) to enrich initial threat information with textual data, allowing for faster correlation of events and identification of security incidents by classifying threats into predefined categories based on textual metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysis is used to categorize malicious activities into threat groups, then the accuracy of threat classification is improved, but the time required for threat analysis increases significantly
Solution Approach 1:
The patent introduces natural language processing technology as an intermediary between raw threat data and human analysts. The NLP system automatically extracts, categorizes, and enriches threat information from unstructured text sources, producing structured classifications that can be quickly generated while maintaining high accuracy through advanced language understanding algorithms.
Solution Approach 2:
The patent replaces the manual mechanical process of human threat analysis with an automated computational system. The NLP-based classification engine processes threat data through algorithmic operations including tokenization, part-of-speech tagging, named entity recognition, and semantic analysis, substituting human cognitive labor with machine-based linguistic processing that operates at much higher speeds.
2Measurement precision
If more information is gathered and processed to improve threat analysis coverage, then the accuracy of threat detection is improved, but the complexity of the analysis process increases
Solution Approach 1:
The patent segments the complex threat analysis process into distinct modular NLP components: text preprocessing module, entity recognition module, relationship extraction module, and classification module. Each module handles a specific aspect of information processing, allowing the system to manage complex analysis tasks through organized, independent functional units that can be developed and maintained separately.
Solution Approach 2:
The patent creates a universal NLP framework that handles multiple types of threat information simultaneously. The same core NLP engine processes various data sources including threat intelligence feeds, security logs, vulnerability databases, and research reports, applying unified linguistic analysis methods across diverse information types to reduce overall system complexity.
Data Source
AI summary
A method and system for classification of cyber-threats is provided. The method includes receiving a request for classifying a cyber-threat detected by a cyber-security system, wherein the request includes initial information about the detected cyber-threat; enriching the initial information about the detected cyber-threat to provide textual information about at least one perceived threat related to the detected cyber-threat; and classifying each of the at least one perceived threat into a security service, wherein the classification is performed based on the respective textual information.