Network Attack Simulation for Vehicular Anomaly Correlation
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Solution Overview
Problem
Vehicular computing networks are susceptible to hacking and unauthorized access due to their dynamic and complex nature, which traditional vulnerability scanners struggle to monitor effectively, leading to undetected security breaches and vulnerabilities.
Innovation Solution
A method involving network attack functions executed against multiple network elements to identify anomalous behavior, correlating these behaviors with specific parameters using artificial intelligence and predefined rules, and storing results in a data lake for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If traditional vulnerability scanners are used to monitor vehicular networks, then device complexity is reduced, but security detection capability deteriorates due to inability to effectively monitor dynamic and complex network environments
Solution Approach 1:
The patent creates a virtual copy of the vehicular network environment using containerized virtual machines that replicate production network elements. This allows security testing and monitoring in a safe, isolated environment that mirrors the actual network structure and behavior, enabling effective detection without requiring complex direct monitoring of the live network.
Solution Approach 2:
The system performs security assessments in advance by executing attack simulations and vulnerability scans on virtual copies before deploying to production. This preliminary action identifies potential security issues proactively, allowing the system to detect threats before they affect the actual vehicular network, reducing the need for complex real-time monitoring.
2Measurement precision
If comprehensive network attack simulations are executed against multiple network elements, then security vulnerability identification improves, but analysis time and processing resources increase
Solution Approach 1:
The patent divides the network into discrete, containerized virtual machines that can be independently tested and analyzed. Each network element is segmented into its own container, allowing parallel execution of attack simulations across multiple elements simultaneously. This segmentation enables comprehensive vulnerability assessment while reducing total analysis time through concurrent processing.
Solution Approach 2:
The system replaces manual, sequential security analysis with automated, parallelized attack simulation frameworks. Machine learning models and automated analysis tools process multiple attack scenarios concurrently, substituting time-consuming manual analysis with efficient computational processing that maintains high accuracy while reducing analysis time.
3Reliability
If real-time monitoring and attack simulation are implemented, then security breach detection improves, but system resource consumption increases
Solution Approach 1:
The patent uses lightweight, containerized virtual machines that can be rapidly created, tested, and destroyed. These disposable virtual environments allow extensive security testing and attack simulation without permanent resource allocation. After each test cycle, the virtual machines are terminated, freeing resources while maintaining high monitoring reliability through repeated, fresh test environments.
Solution Approach 2:
The system implements periodic security assessments rather than continuous full-scale monitoring. Attack simulations are executed at scheduled intervals on virtual copies of the network, providing reliable security detection while consuming computational resources only during assessment windows. This periodic approach balances monitoring reliability with resource conservation.
Data Source
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AI summary
Embodiments of the present disclosure include a method of analyzing the results of a network attack function within an loT environment, for example a vehicular, residential, or industrial computing environment including two or more network elements each with at least one known parameter, the method including a) executing one or more network attack functions against two or more known network elements; b) analyzing results of at least one network attack function to identify anomalous behavior of at least one network element; and c) correlating the identified anomalous behavior of the at least one network element with a specific network attack function permutation and with at least one parameter of the specific network element. In some embodiments, the one or more network attack functions includes a set of attack function permutations.