Malware Propagation Simulation for Accurate R0 Measurement
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Solution Overview
Problem
Conventional malware protection mechanisms are reactive and fail to provide timely detection and mitigation, making it difficult to determine the reproduction number (R0) of malware infections accurately, which hinders effective preventive measures.
Innovation Solution
A simulation method is employed to model malware propagation through computer networks, allowing for the determination of R0 by simulating infection processes and deploying protective measures to inhibit malware spread, thereby enabling proactive protection strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reactive malware protection mechanisms are used, then implementation simplicity is maintained, but timely detection and accurate determination of reproduction number are lost
Solution Approach 1:
The patent creates a simulated copy of the malware propagation process in a controlled environment. Instead of observing real malware infections directly, the system replicates the infection dynamics through simulation, allowing accurate measurement of reproduction numbers without the risks and complexities of real-world observation.
Solution Approach 2:
The simulation system performs preliminary modeling and analysis of malware propagation before actual infections occur. By pre-establishing simulation frameworks and protocols, the system can accurately determine reproduction numbers in advance, enabling proactive rather than reactive protection measures.
2Measurement precision
If simulation methods are employed to determine R0, then measurement precision is improved, but computational resources and time are increased
Solution Approach 1:
The simulation system performs computations for a sufficient number of iterations to achieve statistical significance for R0 determination, rather than attempting exhaustive simulation of all possible infection scenarios. This partial action approach provides accurate enough measurements without the prohibitive time cost of complete enumeration.
Solution Approach 2:
The system varies simulation parameters such as network topology, infection rates, and protective measure effectiveness to efficiently characterize malware behavior. By changing key parameters systematically, the simulation achieves comprehensive understanding of propagation dynamics without requiring exhaustive simulation of every possible condition.
3Reliability
If responsive measures are deployed to inhibit malware propagation, then protection effectiveness is improved, but system complexity and operational overhead are increased
Solution Approach 1:
The simulation system incorporates feedback loops where the effects of protective measures are continuously monitored and fed back into the simulation model. This allows automatic adjustment and optimization of protection strategies based on simulated outcomes, reducing the operational complexity of deploying and managing protective measures while maintaining high effectiveness.
Solution Approach 2:
The simulation framework enables automatic evaluation and selection of optimal protective measures without requiring extensive manual intervention. The system self-adjusts by comparing different protection strategies in simulation and automatically identifies the most effective approaches, simplifying the operational burden on users.
Data Source
AI summary
A computer-implemented method of simulating a propagation of a malware through a set of computer systems, the method comprising: identifying a plurality of first simulated computer systems infected with a simulated malware; for each of the first simulated computer systems, infecting a number of neighbouring second simulated computer systems, the number being zero or an integer; and determining a value of a reproduction number, R0, based on the total number of second simulated computer systems and the number of first simulated computer systems.