AI Security System Automating Threat Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional computer network security relies heavily on human experts, which is inadequate in the face of rapid technological advancements and malicious activities, necessitating the development of artificial intelligence-powered solutions that can mimic human thought processes and be implemented in computer hardware.
Innovation Solution
A computer security system utilizing a network of agents that report hacker activity, a Managed Network & Security Services Provider (MNSP) with components like Logically Inferred Zero-database A-priori Realtime Defense (LIZARD), Artificial Security Threat (AST), Creativity Module, and Critical Thinking Memory & Perception (CTMP) to analyze traffic, detect threats, and make security decisions autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional security methods relying on human experts are used, then security decisions can be made with human judgment, but the system cannot keep pace with rapid technological advancements and malicious activities
Solution Approach 1:
The security system performs self-learning and self-improvement through machine learning algorithms that automatically analyze security data, detect patterns, and update threat detection models without requiring constant human intervention. The system serves itself by continuously adapting to new threats while maintaining reliable security effectiveness.
Solution Approach 2:
The patent replaces manual human expert analysis with automated machine learning systems that process security data at machine speed. The mechanical process of human judgment is substituted with computational algorithms that can rapidly adapt to new threats while maintaining consistent and reliable security decision-making.
2Productivity
If AI-powered automated security systems are implemented, then the system can respond to threats in real-time and scale effectively, but the system complexity increases significantly
Solution Approach 1:
The complex AI security system is divided into modular components including separate machine learning models for different threat types, distinct data processing pipelines, and independent deployment units. This segmentation allows the system to achieve high productivity through parallel processing while managing complexity through modular architecture that can be developed and maintained independently.
3Measurement precision
If comprehensive traffic analysis is performed to detect all threats, then detection precision improves, but processing time and computational resources increase
Solution Approach 1:
The security system applies different levels of analysis intensity to different types of network traffic based on their risk profiles. High-priority traffic patterns receive comprehensive analysis with multiple machine learning models for maximum detection precision, while low-risk traffic receives streamlined analysis. This local quality approach maintains high detection precision for critical threats while reducing average processing time through selective resource allocation.
Data Source
AI summary
COMPUTER SECURITY SYSTEM BASED ON ARTIFICIAL INTELLIGENCE includes Critical Infrastructure Protection & Retribution (CIPR) through Cloud & Tiered Information Security (CTIS), Machine Clandestine Intelligence (MACINT) & Retribution through Covert Operations in Cyberspace, Logically Inferred Zero-database A-priori Realtime Defense (LIZARD), Critical Thinking Memory & Perception (CTMP), Lexical Objectivity Mining (LOM), Linear Atomic Quantum Information Transfer (LAQIT) and Universal BCHAIN Everything Connections (UBEC) system with Base Connection Harmonization Attaching Integrated Nodes.