ANN Security System for Dynamic Threat Adaptation
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Solution Overview
Problem
Conventional security systems are static and limited in their ability to dynamically adapt to emerging threats, often resulting in false positives and inefficiencies due to outdated knowledge bases and lack of dynamic rule updates, which can lead to ineffective identification and handling of security breaches.
Innovation Solution
A security system utilizing an artificial neural network (ANN) model that receives reputation data and intrusion information to generate test cases, simulate topologies, and dynamically update security rules, enabling real-time adaptation and improved threat detection and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional static security systems are used, then system simplicity is maintained, but the ability to adapt to emerging threats deteriorates
Solution Approach 1:
The patent implements dynamic security rules that automatically update based on learned threat patterns. The system transitions from static, pre-defined security policies to dynamic rules that adapt in real-time through machine learning models analyzing network traffic patterns, enabling the system to respond to emerging threats without manual intervention.
Solution Approach 2:
The security system performs self-learning and self-updating through automated machine learning models. The system autonomously analyzes network traffic, identifies threat patterns, generates updated security rules, and validates them without requiring external human input, enabling continuous adaptation to new threats while maintaining operational simplicity.
2Measurement precision
If dynamic rule updates are implemented, then threat detection accuracy improves, but false positive identification increases
Solution Approach 1:
The system incorporates feedback loops where security decisions and their outcomes are continuously analyzed. The machine learning models learn from both true threats and false positives, adjusting rule sensitivity and specificity over time. This feedback mechanism enables the system to improve threat detection accuracy while simultaneously reducing false positives through iterative optimization.
Solution Approach 2:
The patent dynamically adjusts security rule parameters such as sensitivity thresholds, confidence levels, and detection criteria based on learned patterns. The system modifies these parameters in real-time to optimize the balance between detecting actual threats and avoiding false positives, allowing adaptive tuning without manual configuration.
3Reliability
If multiple data feeds are analyzed, then detection comprehensiveness improves, but processing time increases
Solution Approach 1:
The patent divides the analysis of multiple data feeds into segmented processing stages. Different machine learning models specialize in analyzing specific data types (network traffic, system logs, threat intelligence), processing them in parallel or staged sequences. This segmentation enables comprehensive multi-source analysis while reducing overall processing time through specialized, optimized processing paths for each data type.
Data Source
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AI summary
This disclosure relates to method and system for managing security vulnerability in a host computer system. In an embodiment, the method may include receiving reputation data with respect to external network traffic data and receiving intrusion data with respect to host system data. The intrusion data may be generated by the host computer system based on the external network traffic data. The method may further include generating a plurality of test cases based on the reputation data and the intrusion data. The test cases, upon simulation, may provide information with respect to security vulnerability in the host computer system. The method may further include determining a set of implementable topologies for the host computer system, based on a simulation of each of the plurality of test cases, using a first artificial neural network (ANN) model to manage the security vulnerability.