Vehicle Network Intrusion Detection With Parallel Modular Queues
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Solution Overview
Problem
Existing intrusion detection systems in vehicle networks face inefficiencies in detecting intrusions due to the increasing complexity and connectivity of vehicle systems, necessitating a more effective and efficient method to identify and respond to security threats.
Innovation Solution
A modular approach using a plurality of modules connected in a plug-in form, where a first module decodes messages, a second module detects intrusions using multiple detection engines, and a third module processes results, all operating in parallel through message queues to enhance detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional sequential intrusion detection methods are used in vehicle networks, then the system structure is simple, but the detection efficiency and responsiveness are insufficient due to increasing vehicle system complexity and connectivity
Solution Approach 1:
The intrusion detection system is divided into multiple independent modules including message acquisition module, decoding module, multiple detection engines (signature-based, anomaly-based, heuristic-based), and result processing module. Each module operates independently and can be processed in parallel, improving detection efficiency while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system transitions from sequential single-threaded processing to multi-threaded parallel processing architecture. Multiple detection engines operate simultaneously on different message streams or different detection algorithms, adding a temporal parallelism dimension that significantly improves detection throughput and responsiveness without proportionally increasing system complexity.
2Speed
If multiple detection engines operate in parallel, then detection speed and responsiveness improve, but system complexity and resource requirements increase
Solution Approach 1:
Different detection engines (signature-based, anomaly-based, heuristic-based) are segmented into separate modular components, each responsible for specific detection tasks. This segmentation allows parallel operation of multiple engines while keeping each engine's complexity manageable and independent, enabling fast detection without overwhelming system complexity.
Solution Approach 2:
The modular detection engine architecture is designed to be universal and configurable, where engines can be dynamically added, removed, or configured based on specific detection needs. This multi-functionality allows the system to adapt to different detection scenarios without redesigning the entire system, maintaining speed while controlling complexity through flexible configuration.
Data Source
AI summary
A method of detecting intrusion in a vehicle network using a plurality of modules is disclosed. The method according to an embodiment includes sequentially acquiring, by a first module that performs decoding among the plurality of modules, a message transmitted within the vehicle network, transmitting, by the first module, the message to a second module that detects intrusion among the plurality of modules, by sequentially decoding the message, and sequentially detecting intrusion with respect to a message output from the first module by the second module. The plurality of modules may each include a message queue, and may perform an operation in parallel based on the message queue.