MODELLING MALWARE PROPAGATION ON THE INTERNET OF THINGS USING AN AGENT BASED APPROACH ON COMPLEX NETWORKS

(Received: 10-Sep.-2019, Revised: 26-Oct.-2019 , Accepted: 10-Nov.-2019)

Authors Karanja Evanson Mwangi, Shedden Masupe, Jeffrey Mandu,

Keywords #Internet of things #Agent-based modelling and simulation #Modelling malware propagation #Large-scale-free networks #Deep-reinforcement learning.

Abstract Malware threat is a major hindrance to efficient information exchange on the Internet of Things (IoT). Modelling malware propagation is one of the most imperative applications aimed at understanding mechanisms for protecting the Internet of Things environment. Internet of Things can be realized using agent-based modelling over complex networks. In this paper, a malware propagation model using agent-based approach and deep-reinforcement learning on scale free network in IoT (SFIoT) is assiduously detailed. The proposed model is named based on transition states as Susceptible-Infected-Immuned-Recovered-Removed (SIIRR) that represents the states of nodes on large-scale complex networks. The reliability of each node is investigated using the Mean Time To Failure (MTTF). The factors considered for MTTF computations are: degree of a node, node mobility rate, node transmission rate and distance between two nodes computed using Euclidean distance. The results illustrate that the model is comparable to previous models on effects of malware propagation in terms of average energy consumption, average infections at time (t), node mobility and propagation speed

References

[1] S.-M. Cheng, W. C. Ao, P.-Y. Chen and K.-C. Chen, "On Modeling Malware Propagation in Generalized Social Networks," IEEE Communications Letters, vol. 15, no. 1, pp. 25–27, 2011, [Online], Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5638768.

[2] S. Sneha, L. Malathi and R. Saranya, "A Survey on Malware Propagation Analysis and Prevention Model," International Journal of Advancements in Technology, vol. 6, no. 1, pp. 1–4, 2015, [Online], Available: http://dx.doi.org/10.4172/0976-4860.1000148.

[3] M. Yasir, M. A. Habib, M. Shahid and M. Ahmad, "Agent-based Modeling and Simulation of Virus on a Scale-free Network," Proceedings of the International Conference on Future Networks and Distributed Systems (ICFNDS ’17), New York, NY, USA: ACM, pp. 59:1–59:6, 2017, [Online], Available: http://doi.acm.org/10.1145/3102304.3109819.

[4] H. Kırer and Y. A. Çırpıcı, "A Survey of Agent-based Approach of Complex Networks," Ekonomik Yaklasim, vol. 27, no. 98, pp. 1–28, 2016, [Online], Available: https://www.ejmanager.com/mnstemps/ 94/94-1404633261.pdf?t=1552958497.

[5] A. M. del Rey, A. H. Encinas, J. M. Vaquero, A. Q. Dios and G. R. Sánchez, "A Cellular Automata Model for Mobile Worm Propagation," International Work-Conference on the Interplay between Natural and Artificial Computation, Springer International Publishing, pp. 107–116, 2015, [Online], Available: http://dx.doi.org/10.1007/978-3-319-18833-1_12.

[6] L. Feng, X. Liao, Q. Han and H. Li, "Dynamical Analysis and Control Strategies on Malware Propagation Model," Applied Mathematical Modelling, vol. 37, no. 16-17, pp. 8225–8236, 2013, [Online], Available: https://doi.org/10.1016/j.apm.2013.03.051.

[7] S. Hosseini, M. Abdollahi Azgomi and A. Rahmani Torkaman, "Agent-based Simulation of the Dynamics of Malware Propagation in Scale-free Networks," Simulation, vol. 92, no. 7, pp. 709–722, 2016, [Online], Available: https://doi.org/10.1177/0037549716656060.

[8] V. Karyotis, "A Markov Random Field Framework for Modeling Malware Propagation in Complex Communications Networks," IEEE Transactions on Dependable and Secure Computing, 2017, [Online], Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7926392.

[9] K. Batool and M. A. Niazi, "Modeling the Internet of Things: A Hybrid Modeling Approach Using Complex Networks and Agent-based Models," Complex Adaptive Systems Modeling, vol. 5, no. 1, p. 4, 2017, [Online], Available: https://doi.org/10.1186/s40294-017-0043-1.

[10] A. Bose and K. G. Shin, "Agent-based Modeling of Malware Dynamics in Heterogeneous Environments," Security and Communication Networks, vol. 6, no. 12, pp. 1576–1589, 2013, [Online], Available: http://doi.acm.org/10.1145/1378600.1378626.

[11] V. Karyotis and S. Papavassiliou, "Macroscopic Malware Propagation Dynamics for Complex Networks with Churn," IEEE Communications Letters, vol. 19, no. 4, pp. 577–580, 2015, [Online]. Available: https://ieeexplore.ieee.org/iel7/4234/5534602/07029645.pdf.

[12] A. M. del Rey, A. H. Encinas, J. M. Vaquero, A. Q. Dios and G. R. Sánchez, "A Method for Malware Propagation in Industrial Critical Infrastructures," Integrated Computer-aided Engineering, vol. 23, no. 3, pp. 255–268, 2016, [Online], Available: http://dx.doi.org/10.3233/ICA-160518.

[13] E. M. Karanja, S. Masupe and J. Mandu, "Internet of Things Malware: A Survey," International Journal of Computer Science & Engineering Survey, vol. 8, no. 3, pp. 1–20, Jun. 2017, [Online], Available: http://aircconline.com/ijcses/V8N3/8317ijcses01.pdf.

[14] S. Hosseini and M. A. Azgomi, "A Model for Malware Propagation in Scale-free Networks-based on Rumor Spreading Process," Computer Networks, vol. 108, pp. 97–107, 2016, [Online], Available: https://doi.org/10.1016/j.comnet.2016.08.010.