مدل‌سازی انتشار بدافزار VEIRV-A در شبکه‌های اینترنت اشیا

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم کامپیوتر، دانشکده ریاضی و علوم کامپیوتر، دانشگاه شهید باهنر، کرمان، ایران.

2 گروه کامپیوتر، واحد شهربابک، دانشگاه آزاد اسلامی، شهربابک، ایران.

چکیده

با تکامل شبکه بی‌سیم نسل پنجم، اینترنت اشیا (IOT) نیز تحول شگفتی در زندگی بشر ایجاد کرده است. از اینرو مطالعه دقیق‌تر فناوری اینترنت اشیا و حوزه‌های مختلف آن از جمله امنیت این حوزه ضروری خواهد بود. در زمینه امنیت شبکه‌های اینترنت اشیا، یکی از مواردی که مطرح شده انتشار بدافزار است. در این مقاله به مدل‌سازی انتشار بدافزار در شبکه‌های اینترنت اشیا با مدل بیماری‌های همه‌گیری آسیب‌پذیر – در معرض آلودگی - آلوده - بهبودیافته - پادزهر پرداخته‌ایم. اثر اعمال پادزهر روی گره‌های بهبودیافته و آسیب‌پذیر، در انتشار بدافزار شبکه‌های اینترنت اشیا مورد بررسی قرار گرفته است. ما بر اساس درجه گره میزان با اهمیت بودن گره را در شبکه تعیین کرده‌ایم و بر اساس این نرخ، گره‌های آسیب‌پذیر در شبکه ایمن شده‌اند. ایمن‌سازی گره‌های آسیب‌پذیر بر مبنای درجه منجر به کاهش انتشار بدافزار در شبکه شده است. حد آستانه اپیدمی انتشار بدفزار (R0) در مدل پیشنهادی محاسبه شده است. مقایسه مدل با دو مدل SIRS و SEIRS نشان داد مدل پیشنهادی نسبت به دو مدل دیگر عملکرد بهتری داشته است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

VEIRV-A malware propagation modeling in the internet of things networks

نویسندگان [English]

  • Soudeh Hosseini 1
  • Elham Asadi 2
1 Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
2 Department of Computer, Shahrbabak Branch, Islamic Azad University, Shahrbabak, Iran.
چکیده [English]

With the evolution of the fifth generation wireless network, the Internet of Things (IoT) has also created a surprising transformation in human life. Therefore, a more detailed study of IoT technology and its various fields, including security, is necessary. In the field of IoT network security, one of the issues that has been raised is the spread of malware. In this paper, we have modeled the spread of malware in IoT networks with the epidemic disease model of vulnerable - exposed to infection - infected - recovered - antidote. The effect of applying antidote to recovered and vulnerable nodes on the spread of malware in IoT networks has been investigated. We have determined the importance of the node in the network based on its degree, and based on this rate, the vulnerable nodes in the network have been secured. Securing nodes based on the degree has led to a reduction in the spread of malware in the network. The epidemic threshold of malware spread (R0) has been calculated in the proposed model. Comparing the model with the SIRS and SEIRS models showed that the proposed model performed better than the other two models.

کلیدواژه‌ها [English]

  • Malware propagation modeling
  • Epidemic model
  • Basic reproduction number
  • Epidemic threshold limit
  • Internet of Things
[1] F. Sadoughi, A. Behmanesh, and N. Sayfouri, “Internet of Things in Medicine: A Systematic Mapping Study,” J. Biomed. Inform., vol. 103, p. 103383, 2020, doi: 10.1016/j.jbi.2020.103383.
[2] C. Sobin, “A Survey on Architecture, Protocols and Challenges in IoT,” Wireless Pers. Commun., vol. 112, no. 3, pp. 1383-1429, 2020, doi: 10.1007/s11277-020-07108-5.
[3] H. Xia, L. Li, X. Cheng, C. Liu, and T. Qiu, “A Dynamic Virus Propagation Model Based on Social Attributes in City IoT,” IEEE Internet Things J., vol. 7, no. 9, pp. 8036-8048, Sept. 2020, doi: 10.1109/JIOT.2020.2990365.
[4] J. Javaid and I. H. Khan, “Internet of Things (IoT) Enabled Healthcare Helps to Take the Challenges of COVID-19 Pandemic,” J. Oral Biol. Craniofacial Res., vol. 11, no. 2, pp. 209-214, 2021, doi: 10.1016/j.jobcr.2021.01.015.
[5] B. Ramphull and S. D. Nagowah, “A Knowledge Model for IoT-Enabled Smart Banking,” J. Knowl. Econ., vol. 15, no. 2, pp. 9174-9206, 2024, doi: 10.1007/s13132-023-01434-2.
[6] M. Moazzami et al., “Internet of Things Architecture for Intelligent Transportation Systems in a Smart City,” in Proc. 3rd Global Power, Energy Commun. Conf. (GPECOM), 2021, pp. 330-335, doi: 10.1109/GPECOM52585.2021.9587692.
[7] J. Fan et al., “The Future of Internet of Things in Agriculture: Plant High-Throughput Phenotypic Platform,” J. Clean. Prod., vol. 280, p. 123651, 2021, doi: 10.1016/j.jclepro.2020.123651.
[8] M. Stoyanova, Y. Nikoloudakis, S. Panagiotakis, E. Pallis, and E. K. Markakis, “A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues,” IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 1191-1221, 2020, doi: 10.1109/COMST.2019.2962586.
[9] L. Li, J. Cui, R. Zhang, H. Xia, and X. Cheng, “Dynamics of Complex Networks: Malware Propagation Modeling and Analysis in Industrial Internet of Things,” IEEE Access, vol. 8, pp. 64184-64192, 2020, doi: 10.1109/ACCESS.2020.2984668.
[10] A. Akrami and M. Parsamanesh, “Investigation of a Mathematical Fuzzy Epidemic Model for the Spread of Coronavirus in a Population,” Soft Comput. J., vol. 11, no. 1, pp. 2-9, 2022, doi: 10.22052/SCJ.2022.246053.1045 [In Persian].
[11] A. Yadollahi and H. Sabaghian-Bidgoli, “A Simulation Model for the Propagation of COVID-19 Virus Based on the Discrete-Time Markov Chain,” Soft Comput. J., vol. 11, no. 2, pp. 88-103, 2023, doi: 10.22052/SCJ.2023.246527.1076 [In Persian].
[12] W. O. Kermack and A. G. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proc. Roy. Soc. Lond. Ser. A, vol. 115, no. 772, pp. 700-721, 1927, doi: 10.1098/rspa.1927.0118.
[13] X. Zhu et al., “Modeling and Analysis of Malware Propagation for Cluster-Based Wireless Sensor Networks,” in Proc. IEEE 6th Int. Conf. Dependability Sensor, Cloud Big Data Syst. Appl. (DependSys), 2020, pp. 49-54, doi: 10.1109/DependSys51298.2020.00010.
[14] H. Xia, L. Li, X. Cheng, X. Cheng, and T. Qiu, “Modeling and Analysis Botnet Propagation in Social Internet of Things,” IEEE Internet Things J., vol. 7, no. 8, pp. 7470-7481, 2020, doi: 10.1109/JIOT.2020.2984662.
[15] A. Lahrouz, A. Settati, H. El Mahjour, M. El Jarroudi, and M. El Fatini, “Global Dynamics of an Epidemic Model with Incomplete Recovery in a Complex Network,” J. Franklin Inst., vol. 357, no. 7, pp. 4414-4436, 2020, doi: 10.1016/j.jfranklin.2020.03.010.
[16] B.-R. Chen, S.-M. Cheng, and M. B. Mwangi, “A Mobility-Based Epidemic Model for IoT Malware Spread,” IEEE Access, vol. 10, pp. 107929-107941, 2022, doi: 10.1109/ACCESS.2022.3213032.
[17] S. Shen et al., “HSIRD: A Model for Characterizing Dynamics of Malware Diffusion in Heterogeneous WSNs,” J. Netw. Comput. Appl., vol. 146, p. 102420, 2019, doi: 10.1016/j.jnca.2019.102420.
[18] S. Kalam and A. K. Keshri, “Epidemic Model on Denial of Service Attack in IoT Network,” in Proc. Int. Conf. IoT Blockchain Technol. (ICIBT), 2022, pp. 1-6, doi: 10.1109/ICIBT52874.2022.9807815.
[19] R. Casado-Vara, M. Severt, A. Diaz-Longueira, A. M. del Rey, and J. L. Calvo-Rolle, “Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach,” Mathematics, vol. 12, no. 2, p. 250, 2024, doi: 10.3390/math12020250.
[20] P. Yadav and A. K. Keshri, “The Dynamics of SEIQR-V Malware Propagation Model in IoT Networks,” in Proc. Int. Conf. IoT Blockchain Technol. (ICIBT), 2022, pp. 1-6, doi: 10.1109/ICIBT52874.2022.9807775.
[21] Z. Yu et al., “SEI2RS Malware Propagation Model Considering Two Infection Rates in Cyber-Physical Systems,” Physica A: Stat. Mech. Appl., vol. 597, p. 127207, 2022, doi: 10.1016/j.physa.2022.127207.
[22] M. Severt, R. Casado-Vara, and A. Martin del Rey, “A Comparison of Monte Carlo-Based and PINN Parameter Estimation Methods for Malware Identification in IoT Networks,” Technologies, vol. 11, no. 5, p. 133, 2023, doi: 10.3390/technologies11050133.