AI Sensor Network for Dynamic Threat Characterization
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Solution Overview
Problem
Current cybersecurity systems struggle to effectively monitor and track cyber-physical assets, especially when changes occur, such as additions or reconfigurations, which require manual updates prone to errors and costly. Additionally, existing systems lack the ability to detect and track malicious activity across the broader internet, particularly from actors who obfuscate their activity.
Innovation Solution
A system and method for AI-controlled sensor networks that employ spatio-temporal context analysis to dynamically characterize threat states. This includes using diverse sensors for detecting suspicious transmissions, building event knowledge graphs, and implementing active response capabilities. The system also integrates data from location-aware sensors, physical security systems, and real-world events to provide comprehensive threat detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates are performed to track asset changes, then asset information can be updated, but the process is costly and prone to human error
Solution Approach 1:
The system enables automated self-updating of asset information through sensor networks that automatically detect and report changes in asset states, eliminating the need for manual scanning and updating processes. The sensor nodes autonomously monitor assets and trigger updates when changes are detected.
Solution Approach 2:
Manual mechanical scanning processes are replaced with automated sensor-based detection systems that continuously monitor assets and transmit data electronically, substituting human-operated mechanical systems with automated electronic monitoring infrastructure.
2Productivity
If traditional IP-based scanning is used to monitor cyber assets, then scanning can be performed, but malicious actors can obfuscate their activity by making systems appear benign
Solution Approach 1:
The system transitions from traditional IP-based scanning to multi-dimensional monitoring that incorporates physical location data, sensor readings, and contextual information. This adds spatial and environmental dimensions to cyber monitoring, enabling detection of malicious activity that attempts to obfuscate itself through IP address camouflage.
Solution Approach 2:
Sensor nodes perform multiple functions simultaneously including cyber scanning, physical environment monitoring, location tracking, and contextual data collection. This multi-functionality enables cross-validation of data sources to identify malicious activity that attempts to appear benign in any single dimension.
3Reliability
If comprehensive sensor networks are deployed for threat detection, then threat detection capability is improved, but system complexity increases
Solution Approach 1:
The comprehensive sensor network is divided into distributed autonomous sensor nodes that each perform localized monitoring and processing. Each node independently manages its own data collection, analysis, and reporting, reducing the complexity burden on central system architecture while maintaining comprehensive threat detection capability across the network.
Data Source
AI summary
A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.