Threat Categorization and Forecasting for Network Security
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Solution Overview
Problem
As networks become increasingly digital and interconnected, managing diverse and severe security risks is challenging, with existing technologies failing to effectively forecast and prioritize security threats, leading to potential data breaches, identity theft, and malware issues.
Innovation Solution
A system that categorizes security threats, analyzes threat data to forecast future occurrences, and designates emerging threats, allowing users to prioritize resources for imminent threats while reducing protections for those nearing the end of their lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive security monitoring is implemented across all threat categories, then network security coverage is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent segments security threats into distinct categories (malware, phishing, DDoS, etc.) and monitors each category separately using dedicated analytical modules. This segmentation allows the system to manage complexity by handling specific threat types independently rather than treating all threats uniformly, thus improving security coverage without proportionally increasing overall system complexity.
Solution Approach 2:
The system dynamically adjusts security monitoring resources based on real-time threat assessments and forecasts. By using machine learning models to predict emerging threats, the system can dynamically allocate analytical power and alerting mechanisms to high-priority threat categories, optimizing security coverage while avoiding constant high-state resource consumption across all categories.
2Reliability
If security resources are allocated to all threat categories equally, then comprehensive protection is achieved, but resource efficiency decreases
Solution Approach 1:
The system performs preliminary analysis and forecasting of threat categories using historical data and machine learning models before allocating security resources. By predicting which threat categories are likely to emerge or increase in severity, the system can pre-position security measures and alerting for those specific categories, achieving comprehensive protection while avoiding wasteful allocation to low-risk categories.
Solution Approach 2:
The system changes the parameter of resource allocation from static/equal distribution to dynamic/proportional distribution based on threat severity scores and forecasted risk levels. This allows the system to maintain comprehensive protection by adjusting allocation parameters according to actual threat landscapes, thereby improving resource efficiency without sacrificing coverage.
3Measurement precision
If reactive security measures are used to respond to known threats, then response accuracy is improved, but ability to prevent emerging threats decreases
Solution Approach 1:
The system performs preliminary forecasting and identification of emerging threat categories using machine learning models that analyze historical threat data, attack patterns, and security trends. By predicting which threat categories are likely to emerge before they manifest, the system can proactively implement preventive measures for those categories, thereby improving threat prevention capability while maintaining response accuracy for known threats through its established detection mechanisms.
Data Source
AI summary
A system may receive a plurality of security threats and categorize each security threat in the plurality of security threats into security threat categories. The system may then determine, based at least in part upon an instance of a security threat category, a future occurrence of the security threat category and determine, based at least in part upon the future occurrence of the security threat category, that the security threat category is an emerging threat.