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Optimal Control of Malware Propagation in IoT Networks

Mousa Tayseer Jafar, Lu-Xing Yang, Gang Li, Xiaofan Yang

TL;DR

This paper addresses the issue of how to mitigate cyber-attacks before the new patch is applied by formulating an optimal control strategy that reduces the impact of malware propagation and minimise the number of infected devices across IoT networks in the smart home.

Abstract

The rapid proliferation of Internet of Things (IoT) devices in recent years has resulted in a significant surge in the number of cyber-attacks targeting these devices. Recent data indicates that the number of such attacks has increased by over 100 percent, highlighting the urgent need for robust cybersecurity measures to mitigate these threats. In addition, a cyber-attack will begin to spread malware across the network once it has successfully compromised an IoT network. However, to mitigate this attack, a new patch must be applied immediately. In reality, the time required to prepare and apply the new patch can vary significantly depending on the nature of the cyber-attack. In this paper, we address the issue of how to mitigate cyber-attacks before the new patch is applied by formulating an optimal control strategy that reduces the impact of malware propagation and minimise the number of infected devices across IoT networks in the smart home. A novel node-based epidemiological model susceptible, infected high, infected low, recover first, and recover complete(SI_HI_LR_FR_C) is established with immediate response state for the restricted environment. After that, the impact of malware on IoT devices using both high and low infected rates will be analyzed. Finally, to illustrate the main results, several numerical analyses are carried out in addition to simulate the real-world scenario of IoT networks in the smart home, we built a dataset to be used in the experiments.

Optimal Control of Malware Propagation in IoT Networks

TL;DR

This paper addresses the issue of how to mitigate cyber-attacks before the new patch is applied by formulating an optimal control strategy that reduces the impact of malware propagation and minimise the number of infected devices across IoT networks in the smart home.

Abstract

The rapid proliferation of Internet of Things (IoT) devices in recent years has resulted in a significant surge in the number of cyber-attacks targeting these devices. Recent data indicates that the number of such attacks has increased by over 100 percent, highlighting the urgent need for robust cybersecurity measures to mitigate these threats. In addition, a cyber-attack will begin to spread malware across the network once it has successfully compromised an IoT network. However, to mitigate this attack, a new patch must be applied immediately. In reality, the time required to prepare and apply the new patch can vary significantly depending on the nature of the cyber-attack. In this paper, we address the issue of how to mitigate cyber-attacks before the new patch is applied by formulating an optimal control strategy that reduces the impact of malware propagation and minimise the number of infected devices across IoT networks in the smart home. A novel node-based epidemiological model susceptible, infected high, infected low, recover first, and recover complete(SI_HI_LR_FR_C) is established with immediate response state for the restricted environment. After that, the impact of malware on IoT devices using both high and low infected rates will be analyzed. Finally, to illustrate the main results, several numerical analyses are carried out in addition to simulate the real-world scenario of IoT networks in the smart home, we built a dataset to be used in the experiments.
Paper Structure (16 sections, 34 equations, 12 figures, 4 tables, 2 algorithms)

This paper contains 16 sections, 34 equations, 12 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Motivation and problem statement
  3. Contributions
  4. Related work
  5. Epidemiology models for malware propagation
  6. Optimal control theoretic approach to mitigate the spread of malware
  7. Formation of a node-level $\mathbf{SI_HI_LR_FR_C}$ model
  8. Optimal control strategy
  9. Existence of an optimal control
  10. The optimality system
  11. Experiments and analysis of the results
  12. The smart home dataset
  13. The proposed algorithm for solving the optimality system:
  14. The proposed algorithm for randomly generating control strategies:
  15. Conclusion
  16. ...and 1 more sections

Figures (12)

  • Figure 1: State transition in the $\mathbf{SI_HI_LR_FR_C}$ model.
  • Figure 2: The smart home dataset.
  • Figure 3: Samples from the optimality system for case 1.
  • Figure 4: Samples from the optimality system for case 2.
  • Figure 5: Samples from the optimality system for case 3.
  • ...and 7 more figures