Connected Vehicle Cybersecurity via Remote Anomaly Detection
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Solution Overview
Problem
Connected and autonomous vehicles are vulnerable to cyber threats, which can lead to vehicle failure, theft, and other malicious activities, posing risks to safety, property, and privacy.
Innovation Solution
A method and system for connected vehicle cybersecurity that involves creating a normal behavior model based on vehicle data from multiple sources, detecting anomalies in real-time, and implementing mitigation actions to secure vehicles and connected car services against cyber threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If connected vehicles are equipped with network access and computerized control systems to enable remote communication and autonomous functions, then the versatility and functionality of vehicles are improved, but the vulnerability to cyber threats and security risks increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting vehicle data from multiple sources and creating a normal behavior model before threats occur. This baseline model enables the system to detect anomalies in real-time, allowing early identification of potential cyber threats before they can compromise vehicle security.
Solution Approach 2:
The patent introduces a remote system as an intermediary layer between the connected vehicles and potential cyber threats. This intermediary continuously monitors vehicle behavior, analyzes data patterns, and implements security measures without requiring direct access to critical vehicle control systems, thereby protecting the vehicle while maintaining connectivity.
2Reliability
If real-time anomaly detection and mitigation systems are implemented to protect connected vehicles from cyber threats, then the security of vehicles is improved, but the complexity of the system increases
Solution Approach 1:
The security system is segmented into distinct functional modules: data collection from multiple sources, normal behavior model creation, real-time anomaly detection, and mitigation action implementation. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining comprehensive security coverage.
Solution Approach 2:
The system employs self-service mechanisms by automatically comparing real-time vehicle data against the established normal behavior model and autonomously determining mitigation actions when anomalies are detected. This automated response reduces the need for complex manual intervention systems while maintaining high security reliability.
3Measurement precision
If comprehensive vehicle data is collected from multiple data sources to create accurate behavior models for threat detection, then the detection precision is improved, but the quantity of data processing and system complexity increases
Solution Approach 1:
The system extracts only the essential and relevant features from the comprehensive vehicle data collected from multiple sources. By focusing on key behavioral parameters and patterns rather than processing all raw data, the system achieves high detection accuracy while significantly reducing the computational burden and data processing requirements.
Data Source
AI summary
A system and method for connected vehicle cybersecurity. A method includes creating, by a remote system, a normal behavior model based on a first set of data including at least one first event with respect to connected vehicles, wherein the first set of data is collected from data sources, wherein the remote system is remote from the fleet of connected vehicles; detecting, by the remote system, an anomaly based on the normal behavior model and a second set of data, the second set of data including a second event with respect to the connected vehicles, wherein each of the first set of data and the second set of data includes vehicle data related to operation of the connected vehicles, wherein each event represents a communication with the connected vehicles; determining, based on the detected anomaly, at least one mitigation action; and causing implementation of the at least one mitigation action.