Network Malware Simulation with Universal Time-Step Propagation
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Solution Overview
Problem
Conventional malware protection mechanisms are reactive and do not effectively simulate the propagation of malware through computer networks, leading to delayed implementation of protective measures.
Innovation Solution
A computer-implemented method simulates malware propagation using a network model with a universal time frame, adjusting transmission, detection, and removal rates based on network traffic and user behavior, incorporating incubation periods and weighting factors to enhance realism and inform proactive protection strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional reactive malware protection mechanisms are used, then implementation is simpler, but response time is delayed until after infection detection
Solution Approach 1:
The system performs preliminary actions by simulating malware propagation through network models before actual infections occur. The simulation predicts which nodes are most likely to be infected and identifies optimal protection measures in advance, enabling proactive deployment of security measures rather than reactive response after detection.
Solution Approach 2:
The system creates a simplified copy or model of the actual network (graph model with nodes and edges) to simulate malware propagation. This virtual model allows testing and analysis of infection scenarios without affecting the real network, enabling safe experimentation and prediction of outbreak patterns.
2Measurement precision
If universal time frame simulation is implemented, then malware propagation understanding is improved, but computational requirements increase
Solution Approach 1:
The simulation divides time into discrete time steps and processes malware propagation events at each step sequentially. This segmentation allows the system to track incubation periods, detection times, and removal actions in discrete intervals, improving accuracy while managing computational load through structured time-based processing.
Solution Approach 2:
The system dynamically adjusts simulation parameters such as transmission rates, detection rates, and removal rates based on the current state of the network and malware propagation phase. This dynamic approach allows accurate modeling of real-world variability in malware behavior and network conditions without requiring exhaustive pre-computation.
3Reliability
If detailed network modeling with multiple parameters is used, then protection measure effectiveness is improved, but model complexity increases
Solution Approach 1:
The system incorporates multiple parameters including transmission rate, detection rate, removal rate, and incubation period to model malware propagation. These parameters can be adjusted and optimized based on specific malware types and network conditions, allowing the model to adapt to different scenarios and improve prediction accuracy while maintaining a structured framework.
Solution Approach 2:
The simulation incorporates feedback mechanisms where the results of malware propagation are used to identify the most effective protection measures. The system analyzes which nodes and edges are most critical in preventing spread, and this feedback informs the deployment of targeted protection measures, continuously improving effectiveness based on simulation outcomes.
Data Source
AI summary
A computer-implemented method of simulating the propagation of malware in a network is provided. The method comprises accessing a model of the network, where the model comprising a plurality of computer nodes and where each computer node of the plurality of computer nodes is connected to at least one edge of a plurality of edges. Each edge of the plurality of edges connects a pair of computer nodes of the plurality of computer nodes. The method further comprises initiating an outbreak of the malware in the model at a predetermined source computer node of the plurality of computer nodes, and propagating the malware through the model of the network from the source computer node over a plurality of step-times. The propagation is determined based on a rate of transmission per step-time for each edge, and each step-time of the plurality of step-times corresponds to a temporal point that is universal across the network, such that at each step-time the malware propagation through the network occurs simultaneously for each edge of the plurality of edges according to the rate of transmission per step-time for each edge.