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December  2015, 8(6): 1301-1309. doi: 10.3934/dcdss.2015.8.1301

The application of improved-DAA for the vehicle network node security in single- and multi-trusted domain

Shuai Ren 1, , Tao Zhang 2, , Fangxia Shi 3, and  Zongzong Lou 1,

1. 

School of Information Engineering, Chang'an University, Xi'an, Shaanxi 710064, China
2. 

School of Electronic and Control Engineering, Chang'an University, Xi'an, Shaanxi 710064
3. 

School of Information Engineering, Xizang Minzu University, Xianyang, Shaanxi 712082

Received  May 2015 Revised  September 2015 Published  December 2015

Similar to network, vehicle-mounted system has its own vulnerability, which can be used by the attackers. Different from the traditional network security technologies, node security is one of the most important technologies of the vehicle network and it is difficult to achieve because of the mobility and flexibility. In this paper, trusted computing and direct anonymous attestation theories are adopted to establish protocol system of trusted vehicle information authentication, thus the security of authentication process for nodes in vehicle network can be improved. First, we use DAA to achieve the identity authentication for the accessor in single-trusted domain. Second, the improved-DAA will be used to try to promote the security situation in multi-trusted domain. It is illustrated that the efficiency of verification can be increased and the possibility of being attacked can be decreased in single-trusted domain. And the execution efficiency in multi-trusted domain can be improved theoretically.
Keywords: Traffic information security, zero-knowledge proof., trusted computing, direct anonymous attestation, vehicle networks.
Mathematics Subject Classification: Primary: 68M12, 94A05; Secondary: 93D0.
Citation: Shuai Ren, Tao Zhang, Fangxia Shi, Zongzong Lou. The application of improved-DAA for the vehicle network node security in single- and multi-trusted domain. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1301-1309. doi: 10.3934/dcdss.2015.8.1301
References:
[1]

S. Astapov and A. Riid, A multistage procedure of mobile vehicle acoustic identification for single-sensor embedded device, International Journal of Electronics and Telecommunications, 59 (2013), 151-160. doi: 10.2478/eletel-2013-0018.  Google Scholar

[2]

E. Brickell and J. Li, Enhanced privacy id: A direct anonymous attestation scheme with enhanced revocation capabilities, Dependable and Secure Computing IEEE Transactions on, (2007), 21-30. doi: 10.1145/1314333.1314337.  Google Scholar

[3]

M. Backes, M. Maffei and D. Unruh, Zero-knowledge in the applied pi-calculus and automated verification of the direct anonymous attestation protocol, Security and Privacy, 2008. SP 2008. IEEE Symposium on IEEE, 7 (2008), 202-215. doi: 10.1109/SP.2008.23.  Google Scholar

[4]

V. Cambazoglu and C. Rohne, Towards adaptive zero-knowledge protocols: A case study with fiat-shamir identification protocol, Computer Science, 6 (2013), 511-520. Google Scholar

[5]

S. Grzonkowski and P. Corcoran, A practical zero-knowledge proof protocol for web applications, Journal of Information Assurance and Security, 9 (2014), p329. Google Scholar

[6]

H. Khiabani, N. B. Idris and J. A. Manan, Unified trust establishment by leveraging remote attestation - modeling and analysis, Information Management and Computer Security, 21 (2013), 360-380. doi: 10.1108/IMCS-11-2012-0062.  Google Scholar

[7]

M. H. Kim, S. Lee and K. C. Lee, Performance evaluation of node-mapping-based flexray-can gateway for in-vehicle networking system, Intelligent Automation and Soft Computing, 21 (2015), 251-263. doi: 10.1080/10798587.2014.981999.  Google Scholar

[8]

N. Kaaniche, E. E. Moustaine and M. Laurent, A Novel Zero-Knowledge Scheme for Proof of Data Possession in Cloud Storage Applications, Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on IEEE, 2 (2014), 522-531. doi: 10.1109/CCGrid.2014.81.  Google Scholar

[9]

K. Lee, H. L. Dong and M. Yung, Sequential aggregate signatures with short public keys: Design, analysis and implementation studies, Lecture Notes in Computer Science, 7778 (2013), 423-442. doi: 10.1007/978-3-642-36362-7_26.  Google Scholar

[10]

A. Malaver, N. Motta, P. Corke and F. Gonzalez, Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases, Sensors, 15 (2015), 4072-4096. doi: 10.3390/s150204072.  Google Scholar

[11]

F. A. Mohamed and E. E. M. Hemayed, Using trusted computing in trusted mail transfer protocol, Security and Communication Networks, 7 (2014), 926-933. doi: 10.1002/sec.804.  Google Scholar

[12]

S. Patel, V. Patel and D. Jinwala, Privacy preserving distributed k-means clustering in malicious model using zero knowledge proof, Lecture Notes in Computer Science, 7753 (2013), 420-431. doi: 10.1007/978-3-642-36071-8_33.  Google Scholar

[13]

J. Suwatthikul, R. Mcmurran and R. P. Jones, Adaptive OSEK Network Management for in-vehicle network fault detection, 2007. ICVES. IEEE International Conference on Vehicular Electronics and Safety, 8 (2007), 1-6. doi: 10.1109/ICVES.2007.4456405.  Google Scholar

[14]

S. Woo, H. J. Jo and D. H. Lee, A practical wireless attack on the connected car and security protocol for in-vehicle can, Intelligent Transportation Systems IEEE Transactions on, 16 (2015), 993-1006. doi: 10.1109/TITS.2014.2351612.  Google Scholar

[15]

Z. Yan, W. Sun, Z. Chen and S. Zhao, A parallel geographical network vehicle routing algorithm on pc clusters, Acta Geodaetica Et Cartographica Sinica, 43 (2014), 753-760. Google Scholar

[16]

M. Ziefle, P. Pappachan, E. M. Jakobs and H. Wallentowitz, Visual and auditory interfaces of advanced driver assistant systems for older drivers, Lecture Notes in Computer Science, 5105 (2008), 62-69. doi: 10.1007/978-3-540-70540-6_8.  Google Scholar

show all references

References:
[1]

S. Astapov and A. Riid, A multistage procedure of mobile vehicle acoustic identification for single-sensor embedded device, International Journal of Electronics and Telecommunications, 59 (2013), 151-160. doi: 10.2478/eletel-2013-0018.  Google Scholar

[2]

E. Brickell and J. Li, Enhanced privacy id: A direct anonymous attestation scheme with enhanced revocation capabilities, Dependable and Secure Computing IEEE Transactions on, (2007), 21-30. doi: 10.1145/1314333.1314337.  Google Scholar

[3]

M. Backes, M. Maffei and D. Unruh, Zero-knowledge in the applied pi-calculus and automated verification of the direct anonymous attestation protocol, Security and Privacy, 2008. SP 2008. IEEE Symposium on IEEE, 7 (2008), 202-215. doi: 10.1109/SP.2008.23.  Google Scholar

[4]

V. Cambazoglu and C. Rohne, Towards adaptive zero-knowledge protocols: A case study with fiat-shamir identification protocol, Computer Science, 6 (2013), 511-520. Google Scholar

[5]

S. Grzonkowski and P. Corcoran, A practical zero-knowledge proof protocol for web applications, Journal of Information Assurance and Security, 9 (2014), p329. Google Scholar

[6]

H. Khiabani, N. B. Idris and J. A. Manan, Unified trust establishment by leveraging remote attestation - modeling and analysis, Information Management and Computer Security, 21 (2013), 360-380. doi: 10.1108/IMCS-11-2012-0062.  Google Scholar

[7]

M. H. Kim, S. Lee and K. C. Lee, Performance evaluation of node-mapping-based flexray-can gateway for in-vehicle networking system, Intelligent Automation and Soft Computing, 21 (2015), 251-263. doi: 10.1080/10798587.2014.981999.  Google Scholar

[8]

N. Kaaniche, E. E. Moustaine and M. Laurent, A Novel Zero-Knowledge Scheme for Proof of Data Possession in Cloud Storage Applications, Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on IEEE, 2 (2014), 522-531. doi: 10.1109/CCGrid.2014.81.  Google Scholar

[9]

K. Lee, H. L. Dong and M. Yung, Sequential aggregate signatures with short public keys: Design, analysis and implementation studies, Lecture Notes in Computer Science, 7778 (2013), 423-442. doi: 10.1007/978-3-642-36362-7_26.  Google Scholar

[10]

A. Malaver, N. Motta, P. Corke and F. Gonzalez, Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases, Sensors, 15 (2015), 4072-4096. doi: 10.3390/s150204072.  Google Scholar

[11]

F. A. Mohamed and E. E. M. Hemayed, Using trusted computing in trusted mail transfer protocol, Security and Communication Networks, 7 (2014), 926-933. doi: 10.1002/sec.804.  Google Scholar

[12]

S. Patel, V. Patel and D. Jinwala, Privacy preserving distributed k-means clustering in malicious model using zero knowledge proof, Lecture Notes in Computer Science, 7753 (2013), 420-431. doi: 10.1007/978-3-642-36071-8_33.  Google Scholar

[13]

J. Suwatthikul, R. Mcmurran and R. P. Jones, Adaptive OSEK Network Management for in-vehicle network fault detection, 2007. ICVES. IEEE International Conference on Vehicular Electronics and Safety, 8 (2007), 1-6. doi: 10.1109/ICVES.2007.4456405.  Google Scholar

[14]

S. Woo, H. J. Jo and D. H. Lee, A practical wireless attack on the connected car and security protocol for in-vehicle can, Intelligent Transportation Systems IEEE Transactions on, 16 (2015), 993-1006. doi: 10.1109/TITS.2014.2351612.  Google Scholar

[15]

Z. Yan, W. Sun, Z. Chen and S. Zhao, A parallel geographical network vehicle routing algorithm on pc clusters, Acta Geodaetica Et Cartographica Sinica, 43 (2014), 753-760. Google Scholar

[16]

M. Ziefle, P. Pappachan, E. M. Jakobs and H. Wallentowitz, Visual and auditory interfaces of advanced driver assistant systems for older drivers, Lecture Notes in Computer Science, 5105 (2008), 62-69. doi: 10.1007/978-3-540-70540-6_8.  Google Scholar

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