Deceptor-in-the-Middle (DitM): Cyber Deception for Security in Wireless Network Virtualization

Wireless virtualization has emerged as a technology of interest to overcome resource sharing problem by allowing multiple virtual networks to access the same physical wireless infrastructure concurrently based on service level agreements (SLAs). The main focus so far has been on improving the efficiency of wireless resource sharing process. From a security perspective, this technology and its variations have not commanded as much attention partly due to the early state of wireless virtualization development and the undefined nature of potential attacks. In this paper, we present a Deceptor-in-the-Middle (DitM) cyber-deception mechanism that leverages the wireless virtualization architecture to enable virtual network operators to learn about the interest and nature of attacks without the attacker being aware and also not compromising the security and quality of service requirements of the virtual network users. We also propose a detection mechanism for identifying the presence of an attacker and redirecting to deception virtual networks. The performance of the proposed approach is evaluated using numerical results obtained from Monte Carlo simulations. The numerical results signify that the proposed DitM results in higher throughput than that of without the deceptor. We also analyzed the DitM using different metrics including the probability of miss detection and false alarm.

[1]  Aggeliki Sgora,et al.  An effective spectrum handoff scheme for Cognitive Radio Ad hoc Networks , 2017, 2017 Wireless Telecommunications Symposium (WTS).

[2]  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.

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

[4]  J. Wolfowitz,et al.  Optimum Character of the Sequential Probability Ratio Test , 1948 .

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

[6]  Yang Zheng,et al.  A scheme against primary user emulation attack based on improved energy detection , 2016, 2016 IEEE International Conference on Information and Automation (ICIA).

[7]  Philippe Ciblat,et al.  Surveillance Strategies Against Primary User Emulation Attack in Cognitive Radio Networks , 2015, IEEE Transactions on Wireless Communications.

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

[9]  Srikanth V. Krishnamurthy,et al.  Cyber Deception: Virtual Networks to Defend Insider Reconnaissance , 2016, MIST@CCS.

[10]  Naima Kaabouch,et al.  A particle swarm optimization based algorithm for primary user emulation attack detection , 2018, 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).

[11]  Abraham O. Fapojuwo,et al.  Security threat assessment of simultaneous multiple Denial-of-Service attacks in IEEE 802.22 Cognitive Radio networks , 2016, 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[12]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[13]  K. J. Ray Liu,et al.  Advances in cognitive radio networks: A survey , 2011, IEEE Journal of Selected Topics in Signal Processing.

[14]  Danda B. Rawat,et al.  Advances on Security Threats and Countermeasures for Cognitive Radio Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[15]  Kostas E. Psannis,et al.  Cognitive Radio Network and Network Service Chaining toward 5G: Challenges and Requirements , 2017, IEEE Communications Magazine.

[16]  Danda B. Rawat,et al.  nROAR: Near Real-Time Opportunistic Spectrum Access and Management in Cloud-Based Database-Driven Cognitive Radio Networks , 2017, IEEE Transactions on Network and Service Management.

[17]  Moshe T. Masonta,et al.  Spectrum Decision in Cognitive Radio Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[18]  Santhanakrishnan Anand,et al.  Detecting Primary User Emulation Attacks in Dynamic Spectrum Access Networks , 2009, 2009 IEEE International Conference on Communications.