The Effect of Key Nodes on the Malware Dynamics in the Industrial Control Network
Source abstract
Abstract
As industrialization and informatization become more deeply intertwined, industrial control networks have entered an era of intelligence. The connection between industrial control networks and the external internet is becoming increasingly close, which leads to frequent security accidents. This paper proposes a model for the industrial control network. It includes a malware containment strategy that integrates intrusion detection, quarantine, and monitoring. Based on this model, the role of key nodes in the spread of malware is studied, a comparison experiment is conducted to validate the impact of the containment strategy. In addition, the dynamic behavior of the model is analyzed, the basic reproduction number is computed, and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number. Moreover, through simulation experiments, the effectiveness of the containment strategy is validated, the influence of the relevant parameters is analyzed, and the containment strategy is optimized. In other words, selective immunity to key nodes can effectively suppress the spread of malware and maintain the stability of industrial control systems. The earlier the immunization of key nodes, the better. Once the time exceeds the threshold, immunizing key nodes is almost ineffective. The analysis provides a better way to contain the malware in the industrial control network.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Complex Network Analysis Techniques · Mathematical and Theoretical Epidemiology and Ecology Models
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (36)
Show source-provided references
1. Industrial cyber vulnerabilities: Lessons from stuxnet and the Internet of Things
2. Night dragon: Cyberwar meets corpoiate espionage
3. The cousins of Stuxnet: Duqu, flame, and gauss10.3390/fi4040971 ↗
4. Modelling Duqu 2.0 malware using attack trees with sequential conjunction
5. PLC-Blaster: A worm living solely in the PLC
6. Blackenergy a threat to industrial control systems network security
7. Deconstructing flame: The limitations of traditional defences10.1016/S1361-3723(12)70102-1 ↗
8. Understanding the mirai botnet
9. Cyber-attack detection in water distribution systems based on blind sources separation technique10.3390/w13060795 ↗
10. An experimental platform for assessing SCADA vulnerabilities and countermeasures in power plants
11. Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review10.1016/j.jnca.2023.103760 ↗
12. Key nodes in complex networks identified by multi-attribute decision-making method10.7498/aps.62.020204 ↗
13. Identifification of influential spreaders in complex networks10.1038/nphys1746 ↗
14. Optimizing pinning control of complex dynamical networks based on spectral properties of grounded laplacian matrices10.1109/TSMC.2018.2882620 ↗
15. Epidemiology: How viruses spread among computers and people10.1126/science.1061076 ↗
16. Advanced malware propagation on random complex networks
17. Agent-based simulation of the dynamics of malware propagation in scale-free networks10.1177/0037549716656060 ↗
18. The research on control strategy of worms spread in complex network in industry10.4028/www.scientific.net/AMR.487.758 ↗
19. Mining user profile exploitation cluster from computer program logs
20. Epidemic on interconnected networks10.1103/PhysRevE.85.066109 ↗
21. Fractional dynamics of stuxnet virus propagation in industrial control systems10.3390/math9172160 ↗
22. Dynamics of complex networks: Malware propagation modeling and analysis in industrial internet of things10.1109/ACCESS.2020.2984668 ↗
23. Joint differential game and double deep Q-networks for suppressing malware spread in industrial Internet of Things10.1109/TIFS.2023.3307956 ↗
24. STSIR: An individual-group game-based model for disclosing virus spread in social Internet of Things10.1016/j.jnca.2023.103608 ↗
25. Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes10.1016/j.dcan.2022.05.004 ↗
26. Deep Q-network-based open-set intrusion detection solution for industrial Internet of Things
27. Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacks10.1016/j.asoc.2023.111080 ↗
28. Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems10.1016/j.eswa.2023.120192 ↗
29. An intrusion-detection model10.1109/TSE.1987.232894 ↗
30. Epidemic spreading on three-layer interdependent networks10.1142/S0218339016500248 ↗
31. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations10.1007/BF00178324 ↗
32. Fu · Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis · 2014 · 63
33. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission10.1016/S0025-5564(02)00108-6 ↗
34. Networks synchronization, diffusion, and the paradox of heterogeneity10.1103/PhysRevE.71.016116 ↗
35. Interplay between malware epidemics and honeynet potency in industrial control system network10.1109/ACCESS.2020.2989612 ↗
36. On the computation of R0 and its role on global stability