Impact of Information based Classification on Network Epidemics.

Area of Science:

• Network Science
• Epidemiology
• Mathematical Modeling

Background:

• Accurately modeling malicious propagation in networks is challenging due to incomplete understanding of underlying processes.
• Existing epidemic frameworks lack symptom-based classification for nuanced analysis.

Purpose of the Study:

• To investigate the impact of information availability on controlling malicious network epidemics.
• To introduce a novel differential epidemic model with symptom-based classification.

Main Methods:

• Development of a 1-n-n-1 type differential epidemic model, termed DifEpGoss, incorporating a five-class system.
• Analysis of epidemic thresholds to determine long-term system behavior.
• Validation using three real-world network datasets and simulation experiments with varying attack/defense strengths.

Main Results:

• The DifEpGoss model demonstrates that classification-based prevention is effective in containing network epidemics.
• Analysis revealed a critical epidemic threshold influencing system dynamics.
• Simulation experiments with 27 attack/defense scenarios corroborated the model's utility.

Conclusions:

• The proposed DifEpGoss architecture provides a valuable framework for understanding and controlling malicious network epidemics.
• Symptom-based classification significantly enhances epidemic containment strategies.
• The model's effectiveness is validated by real-world data and extensive simulations.