Performance evaluation of deception system for deceiving cyber adversaries in adaptive virtualized wireless networks

Malicious actions by cyber-adversaries are growing exponentially which makes it difficult to combat cyber-attacks for emerging networked cyber physical systems (CPS) and Internet of Things (IoT). Furthermore, wireless networks - major communication media for most emerging CPS and IoT applications - are highly vulnerable to cyber attacks because of their nature of open communications. In this paper, we evaluate the performance of the cyber deception system to combat cyber adversaries in virtualized wireless networking framework where software defined network (SDN) controller creates mobile virtual network operators (MVNOs) and continuously senses the network, observes the connections and creates deception MVNO to direct cyber adversaries. The deception MVNO can be used to learn about cyber adversaries in terms of their capabilities, intent and how much damage they can do in the system and so on. Thus, the cyber deception can help secure legitimate users from cyber adversaries. Performance of the proposed approach is evaluated with results obtained from Monte Carlo simulations.

[1]  Gábor Lugosi,et al.  Prediction, learning, and games , 2006 .

[2]  D. Kendall On the Generalized "Birth-and-Death" Process , 1948 .

[3]  Danda B. Rawat,et al.  Payoff Optimization Through Wireless Network Virtualization for IoT Applications: A Three Layer Game Approach , 2019, IEEE Internet of Things Journal.

[4]  Sachin Shetty,et al.  Dynamic Spectrum Access for Wireless Networks , 2015, SpringerBriefs in Electrical and Computer Engineering.

[5]  Yitzchak M. Gottlieb,et al.  ACyDS: An adaptive cyber deception system , 2016, MILCOM 2016 - 2016 IEEE Military Communications Conference.

[6]  F. Richard Yu,et al.  Wireless Network Virtualization: A Survey, Some Research Issues and Challenges , 2015, IEEE Communications Surveys & Tutorials.

[7]  Danda B. Rawat,et al.  Wireless network virtualization for enhancing security: Status, challenges and perspectives , 2016, SoutheastCon 2016.

[8]  Peter Auer,et al.  Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring , 2006, ALT.

[9]  Danda B. Rawat,et al.  Cyber-Physical Systems: From Theory to Practice , 2015 .

[10]  Danda B. Rawat,et al.  On the wireless virtualization with QoE constraints , 2019, Trans. Emerg. Telecommun. Technol..

[11]  Xin Wang,et al.  Wireless network virtualization , 2013, 2013 International Conference on Computing, Networking and Communications (ICNC).

[12]  Ashok Kumar Mohan,et al.  Deceiving Attackers in Wireless Local AreaNetworks Using Decoys , 2018 .