Wireless Physical Layer Characteristics Based Random Number Generator: Hijack Attackers

Random numbers are widely used in 5G communication security. In this paper, we propose a wireless physical layer (PHY-layer) characteristics based random number generator in vehicular networks. Firstly, the closed form expression of random transmission success probability is derived under the presence of multiple jamming attackers in a Nakagami-m fading channel. Secondly, a novel Random Transmission Success Probability based Physical Random Number Generator (RTSP-PhRNG) is presented. Finally, numerical results are conducted and a Universal Software Radio Peripheral (USRP) based prototype is implemented to validate our proposed method. Furthermore, the standard randomness test suite from NIST shows that our proposed PhRNG reveals good randomness.

[1]  Peng Ning,et al.  Authenticating Primary Users' Signals in Cognitive Radio Networks via Integrated Cryptographic and Wireless Link Signatures , 2010, 2010 IEEE Symposium on Security and Privacy.

[2]  Elaine B. Barker,et al.  A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications , 2000 .

[3]  Tongtong Li,et al.  Mitigating primary user emulation attacks in cognitive radio networks using advanced encryption standard , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[4]  Chen-Mou Cheng,et al.  Practical Physical Layer Security Schemes for MIMO-OFDM Systems Using Precoding Matrix Indices , 2013, IEEE Journal on Selected Areas in Communications.

[5]  Emad Alsusa,et al.  Physical Layer Secret Key Exchange Using Phase Randomization in MIMO-OFDM , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[6]  Wenyuan Xu,et al.  The feasibility of launching and detecting jamming attacks in wireless networks , 2005, MobiHoc '05.

[7]  Donald F. Towsley,et al.  Physical Layer Security in Heterogeneous Cellular Networks , 2016, IEEE Transactions on Communications.

[8]  B. M. Rogina,et al.  Quantum random number generator based on photonic emission in semiconductors. , 2006, The Review of scientific instruments.

[9]  Giuseppe Lo Re,et al.  A TRNG exploiting multi-source physical data , 2010, Q2SWinet '10.

[10]  Andrew Thangaraj,et al.  Robustness of Physical Layer Security Primitives Against Attacks on Pseudorandom Generators , 2014, IEEE Transactions on Communications.

[11]  M. Haenggi,et al.  Interference in Large Wireless Networks , 2009, Found. Trends Netw..

[12]  Zhuo Lu,et al.  Modeling, Evaluation and Detection of Jamming Attacks in Time-Critical Wireless Applications , 2014, IEEE Transactions on Mobile Computing.

[13]  L. Slater,et al.  Confluent Hypergeometric Functions , 1961 .

[14]  John E. Angus,et al.  The Probability Integral Transform and Related Results , 1994, SIAM Rev..

[15]  Lifeng Wang,et al.  Safeguarding 5G wireless communication networks using physical layer security , 2015, IEEE Communications Magazine.

[16]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .