Cyber-Attack Simulation for Sensor-Based Device Security
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Solution Overview
Problem
Smart sensor devices in IoT systems are vulnerable to cyber-attacks due to their ease of connectivity, which poses security and privacy concerns, and existing solutions often burden performance or are difficult to implement effectively.
Innovation Solution
A computer-implemented method that simulates cyber-attacks and mitigating processes based on input parameters to determine risk levels, using natural language processing and machine learning to estimate the time required for each, allowing for targeted mitigation strategies without compromising device performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If smart sensor devices are made easily connectable to enable IoT functionality, then device versatility and ease of operation are improved, but vulnerability to cyber-attacks increases
Solution Approach 1:
The system performs preliminary simulations of cyber-attacks and mitigating processes before actual threats occur. By pre-calculating attack scenarios and their mitigation strategies based on simulated data, the system prepares defense mechanisms in advance, allowing smart sensor devices to maintain connectivity while being preemptively protected against vulnerabilities.
2Reliability
If comprehensive security mitigations are implemented to protect against cyber-attacks, then device security is improved, but device performance and operational efficiency deteriorate
Solution Approach 1:
The system applies security mitigations selectively based on local risk assessments. Instead of implementing comprehensive security measures uniformly across all device functions, it identifies specific vulnerable features and applies targeted mitigations only where needed. This localized approach maintains device performance in secure areas while providing enhanced protection where vulnerabilities exist.
Solution Approach 2:
The system dynamically adjusts security parameters based on simulated risk levels. By changing security configuration parameters according to the severity and probability of detected attack scenarios, the system optimizes the balance between security and performance. Less restrictive parameters are applied to low-risk functions while stricter parameters are enforced only where simulations indicate significant vulnerability.
3Reliability
If multiple security mitigating processes are implemented to address different cyber-attack vectors, then security coverage is improved, but system complexity increases
Solution Approach 1:
The security system is segmented into distinct modular components, each handling specific attack vectors or mitigation functions. The simulation framework divides cyber-attack scenarios into separate categories (e.g., data breaches, unauthorized access, communication interception), and assigns dedicated mitigation processes to each segment. This segmentation allows comprehensive security coverage while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The simulation system acts as an intermediary between threat detection and mitigation implementation. It processes attack scenarios, calculates risk levels, and selects appropriate mitigations without requiring direct complex interactions between all security components. This intermediary layer simplifies the overall system architecture by centralizing the decision-making logic for security measure selection and execution.
Data Source
AI summary
Aspects of the invention include a computer-implemented method, including performing simulations of a form of cyber-attack based on different input parameters to determine a respective time to perform each cyber-attack on a plurality of features of a sensor-based device. Additionally, performing simulations of a plurality of mitigating processes for each cyber-attack based on different input parameters to determine a respective time to perform each mitigating process. An associated risk level of each cyber-attack is determined based at least in part on the simulations. A mitigation process is selected based at least in part on the associated risk levels.