AI Anomaly Detection for Connected Aircraft Network Traffic
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Solution Overview
Problem
Current cybersecurity solutions for connected vehicles lack real-time and autonomous capabilities, requiring significant human intervention and resources, which are costly and inefficient in responding to cyber threats.
Innovation Solution
A cloud-based learning model is used to monitor network traffic of connected vehicles, generating an anomaly prediction model through AI and machine learning techniques, enabling real-time detection and prevention of cyberattacks by filtering and normalizing network data, and updating the model with real-time information to block malicious packets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If third-party cyber-threat intelligence solutions are implemented, then security monitoring capability is improved, but response time is delayed and human resources are significantly required
Solution Approach 1:
The system implements autonomous anomaly detection through machine learning models that automatically analyze network traffic patterns and identify security threats without requiring human intervention. The model continuously learns from historical data and autonomously generates alerts, enabling the system to monitor and respond to cyber threats independently, thereby eliminating the need for manual security operations center involvement and achieving real-time response.
Solution Approach 2:
The patent replaces manual security analysis and human-operated monitoring systems with automated machine learning algorithms. The ML model processes network traffic data, identifies anomalies, and generates security alerts automatically, substituting the mechanical human-operated security operations center with an intelligent automated system that operates continuously without fatigue or delay.
2Measurement precision
If Security Operation Center and Incident Response teams are implemented, then threat detection accuracy is improved, but operational costs and resource requirements increase significantly
Solution Approach 1:
The system employs self-learning machine learning models that automatically improve their threat detection accuracy over time by continuously analyzing network traffic patterns and learning from new threats. The autonomous model eliminates the need for complex human-operated security teams while maintaining high detection accuracy through automated pattern recognition and anomaly identification.
Solution Approach 2:
The patent utilizes machine learning models that dynamically adjust their detection parameters and thresholds based on learned patterns from historical data. The system adapts its sensitivity and detection criteria automatically, replacing the need for manual tuning by security experts while maintaining optimal detection accuracy through data-driven parameter optimization.
3Loss of information
If traditional cybersecurity monitoring systems are used, then comprehensive security analysis is achieved, but real-time autonomous response is not possible
Solution Approach 1:
The system implements fully autonomous security monitoring where machine learning models independently analyze network traffic, identify threats, and generate alerts without human intervention. The self-learning capability ensures comprehensive security analysis while the automated nature enables real-time response, simultaneously achieving both complete security monitoring and autonomous operation.
Solution Approach 2:
The patent employs continuously operating machine learning models that process network traffic in real-time without interruption. The system maintains constant security monitoring and can immediately respond to threats as they are detected, ensuring both comprehensive analysis coverage and continuous autonomous operation without the delays inherent in manual security operations.
Data Source
AI summary
A method of monitoring network traffic of a connected vehicle. The method includes receiving network traffic information from a vehicle gateway, the network traffic information including malicious and/or benign information. The method also includes storing the network traffic information on a data server and periodically updating the network traffic information stored on the data server. The method further includes: pre-processing the network traffic information, the pre-processing the network traffic information including filtering and normalizing the network traffic information; generating a learning model based on the pre-processed network traffic information, the learning model being generated by an artificial intelligence learning; updating the learning model based on additional network traffic information, the additional network traffic information including real-time network data; in accordance with the updated learning model, detecting an anomaly event in the incoming network data; and generating a notification and/or blocking one or more packets associated with the incoming network data.