Ramazan Algin
ABSTRACT
In Advanced Metering Infrastructure (AMI) networks, power data collections from smart meters are static. Due to such static nature, attackers may predict the transmission behavior of the smart meters which can be used to launch selective jamming attacks that can block the transmissions. To avoid such attack scenarios and increase the resilience of the AMI networks, in this paper, we propose dynamic data reporting schedules for smart meters based on the idea of moving target defense (MTD) paradigm. The idea behind MTD-based schedules is to randomize the transmission times so that the attackers will not be able to guess these schedules. Specifically, we assign a time slot for each smart meter and in each round we shuffle the slots with Fisher-Yates shuffle algorithm that has been shown to provide secure randomness. We also take into account the periodicity of the data transmissions that may be needed by the utility company. With the proposed approach, a smart meter is guaranteed to send its data at a different time slot in each round. We implemented the proposed approach in ns-3 using IEEE 802.11s wireless mesh standard as the communication infrastructure. Simulation results showed that our protocol can secure the network from the selective jamming attacks without sacrificing performance by providing similar or even better performance for collection time, packet delivery ratio and end-to-end delay compared to previously proposed protocols.
- Muhammad Qasim Ali, Ehab Al-Shaer, and Qi Duan. 2013. Randomizing AMI configuration for proactive defense in smart grid. In Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on. IEEE, 618--623. Google Scholar
Cross Ref
- Kirill Andreev and Pavel Boyko. 2010. IEEE 802.11 s mesh networking NS-3 model. In Workshop on ns3, Vol. 43.Google Scholar
- Lenore Blum, Manuel Blum, and Mike Shub. 1986. A simple unpredictable pseudorandom number generator. SIAM Journal on computing 15, 2 (1986), 364--383. Google ScholarDigital Library
- Ralf Burda and Christian Wietfeld. 2007. Multimedia over 802.15. 4 and ZigBee networks for ambient environment control. In Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th. IEEE, 179--183.Google ScholarCross Ref
- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2001. Introduction to algorithms second edition. (2001).Google Scholar
- Richard Durstenfeld. 1964. Algorithm 235: random permutation. Commun. ACM 7, 7 (1964), 420.Google ScholarDigital Library
- Etimad Fadel, Vehbi C. Gungor, Laila Nassef, Nadine Akkari, MG Abbas Malik, Suleiman Almasri, and Ian F. Akyildiz. 2015. A survey on wireless sensor networks for smart grid. Computer Communications 71 (2015), 22--33. Google ScholarDigital Library
- Ronald Aylmer Fisher, Frank Yates, et al. 1938. Statistical tables for biological, agricultural and medical research. (1938).Google Scholar
- Jingcheng Gao, Yang Xiao, Jing Liu, Wei Liang, and CL Philip Chen. 2012. A survey of communication/networking in smart grids. Future Generation Computer Systems 28, 2 (2012), 391--404. Google ScholarDigital Library
- IEEE 802.11 Working Group et al. 2010. IEEE Standard for Information Technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications Amendment 6: Wireless Access in Vehicular Environments. IEEE Std 802, 11 (2010).Google Scholar
- Vehbi C Gungor, Dilan Sahin, Taskin Kocak, Salih Ergut, Concettina Buccella, Carlo Cecati, and Gerhard P. Hancke. 2011. Smart grid technologies: Communication technologies and standards. IEEE transactions on Industrial informatics 7, 4 (2011), 529--539.Google Scholar
- Miguel Herrero-Collantes and Juan Carlos Garcia-Escartin. 2017. Quantum random number generators. Reviews of Modern Physics 89, 1 (2017), 015004.Google ScholarCross Ref
- Jaebeom Kim, Dabin Kim, Keun-Woo Lim, Young-Bae Ko, and Sang-Youm Lee. 2012. Improving the reliability of IEEE 802.11 s based wireless mesh networks for smart grid systems. Journal of Communications and Networks 14, 6 (2012), 629--639. Google ScholarCross Ref
- Min Seok Kim, Sung Ryul Kim, Jeonghyun Kim, and Younghwan Yoo. 2011. Design and implementation of MAC protocol for SmartGrid HAN environment. In Computer and Information Technology (CIT), 2011 IEEE 11th International Conference on. IEEE, 212--217. Google ScholarDigital Library
- Murat Kuzlu, Manisa Pipattanasomporn, and Saifur Rahman. 2014. Communication network requirements for major smart grid applications in HAN, NAN and WAN. Computer Networks 67 (2014), 74--88. Google ScholarCross Ref
- Claire Le Goues, Anh Nguyen-Tuong, Hao Chen, Jack W. Davidson, Stephanie Forrest, Jason D. Hiser, John C. Knight, and Matthew Van Gundy. 2013. Moving target defenses in the helix self-regenerative architecture. In Moving Target Defense II. Springer, 117--149. Google ScholarCross Ref
- Bratislav Milic and Miroslaw Malek. 2009. NPART-node placement algorithm for realistic topologies in wireless multihop network simulation. In Proceedings of the 2nd international conference on simulation tools and techniques. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 9.Google ScholarDigital Library
- Alejandro Proano and Loukas Lazos. 2010. Selective jamming attacks in wireless networks. In Communications (ICC), 2010 IEEE International Conference on. IEEE, 1--6. Google ScholarCross Ref
- Nico Saputro et al. 2017. Investigation of Smart Meter Data Reporting Strategies for Optimized Performance in Smart Grid AMI Networks. IEEE Internet of Things Journal (2017).Google Scholar
- Nico Saputro and Kemal Akkaya. 2014. Periodic data reporting strategies for IEEE 802.11 s-based smart grid AMI networks. In Smart Grid Communications (SmartGridComm), 2014 IEEE International Conference on. IEEE, 314--319.Google ScholarCross Ref
- Nico Saputro, Kemal Akkaya, and Suleyman Uludag. 2012. A survey of routing protocols for smart grid communications. Computer Networks 56, 11 (2012), 2742--2771. Google ScholarDigital Library
- Marco Tiloca, Domenico De Guglielmo, Gianluca Dini, and Giuseppe Anastasi. 2013. SAD-SJ: A self-adaptive decentralized solution against Selective Jamming attack in Wireless Sensor Networks. In Emerging Technologies & Factory Automation (ETFA), 2013 IEEE 18th Conference on. IEEE, 1--8. Google ScholarCross Ref
- Samet Tonyali, Ozan Cakmak, Kemal Akkaya, Mohamed MEA Mahmoud, and Ismail Guvenc. 2016. Secure data obfuscation scheme to enable privacy-preserving state estimation in smart grid AMI networks. IEEE Internet of Things Journal 3, 5 (2016), 709--719. Google ScholarCross Ref
- Umesh V Vazirani and Vijay V. Vazirani. 1984. Efficient and secure pseudorandom number generation. In Foundations of Computer Science, 1984. 25th Annual Symposium on. IEEE, 458--463.Google Scholar
- Jun Xu, Pinyao Guo, Mingyi Zhao, Robert F. Erbacher, Minghui Zhu, and Peng Liu. 2014. Comparing different moving target defense techniques. In Proceedings of the First ACM Workshop on Moving Target Defense. ACM, 97--107. Google ScholarDigital Library
- Wenyuan Xu, Wade Trappe, Yanyong Zhang, and Timothy Wood. 2005. The feasibility of launching and detecting jamming attacks in wireless networks. In Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing. ACM, 46--57. Google ScholarDigital Library
- Rui Zhuang, Su Zhang, Alex Bardas, Scott A. DeLoach, Xinming Ou, and Anoop Singhal. 2013. Investigating the application of moving target defenses to network security. In Resilient Control Systems (ISRCS), 2013 6th International Symposium on. IEEE, 162--169. Google ScholarCross Ref
Index Terms
- Mitigating Selective Jamming Attacks in Smart Meter Data Collection using Moving Target Defense
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