Novel IOV worm model and its corresponding hybrid anti-worm strategy in expressway interchange terminal

Zheng Wang , Huan-yan Qian , Jing-ya Wang , Song Gao , Yan-gui Xu

Journal of Central South University ›› 2013, Vol. 20 ›› Issue (5) : 1259 -1268.

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Journal of Central South University ›› 2013, Vol. 20 ›› Issue (5) : 1259 -1268. DOI: 10.1007/s11771-013-1610-z
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Novel IOV worm model and its corresponding hybrid anti-worm strategy in expressway interchange terminal

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Abstract

In order to take precaution and cure against internet of vehicles (IOV) worm propagation in expressway, the IOV worm propagation and its corresponding anti-worm strategy were studied in expressway interchange terminal. According to omnirange driving in expressway interchange terminal and vehicular mobile communication environment, an IOV worm propagation model is constructed; and then according to the dynamic propagation law and destructiveness of IOV worm in this environment, a novel hybrid anti-worm strategy for confrontation is designed. This worm propagation model can factually simulates the IOV worm propagation in this interchange terminal environment; and this hybrid anti-worm strategy can effectively control IOV worm propagation in the environment, moreover, it can reduce the influence on network resource overhead.

Keywords

internet of vehicles / worm propagation model / hybrid anti-worm strategy / expressway / interchange terminal

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Zheng Wang, Huan-yan Qian, Jing-ya Wang, Song Gao, Yan-gui Xu. Novel IOV worm model and its corresponding hybrid anti-worm strategy in expressway interchange terminal. Journal of Central South University, 2013, 20(5): 1259-1268 DOI:10.1007/s11771-013-1610-z

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References

[1]

LinC, ShakyaR. Worm spreading and patching in inter-vehicle communications [J]. International Journal of Communication Networks and Information Security, 2010, 2(1): 50-53

[2]

WangX-m, LiQ-l, LiY-shu. EiSIRS: A formal model to analyze the dynamics of worm propagation in wireless sensor networks [J]. Journal of Combinatorial Optimization, 2010, 20(1): 47-62

[3]

GiannetsosT, DimitrlouT, NeelirR. Self-propagating worms in wireless sensor networks [C]. Proceedings of the Co-Next Student Workshop 09, 2009New York, NY, USAACM31-32

[4]

SunB, YanG-h, XiaoYang. Worm propagation dynamics in wireless sensor networks [C]. Proceedings of the ICC’08, 2008Beijing, ChinaIEEE Communications Society Press1541-1545

[5]

MohimaniG H, AshtianiF, JavanmaardA, HamdiM. Mobility modeling, spatial traffic distribution, and probability of connectivity for sparse and dense vehicular ad hoc networks [J]. IEEE Transactions on Vehicular Technology, 2009, 58(4): 1998-2007

[6]

ChenX-q, XieW-j, ShiJ, ShiQ-xin. Perturbation and stability analysis of the multi-anticipative intelligent driver model [J]. International Journal of Modern Physics C, 2010, 21(5): 647-668

[7]

FrankC, EmreC, XuJ. Worm vs. w worm: Preliminary study of an active counter-attack mechanism [C]. Proceedings of the 2004 ACM workshop on rapid malcode, 2004Washington DC, USAACM83-93

[8]

LiJ-q, QinZ, OuL, SalmanO, LiuA X, YangJ-min. Modeling and analysis of gradual hybrid anti-worm [J]. Journal of Central South University of Technology, 2011, 18(6): 2050-2055

[9]

FangY-h, ZhengX-f, XieT-tang. A revised benign worm-anti-worm propagation model [J]. Applied Mechanics and Materials, 2011, 121/126: 4340-4344

[10]

ZhouH-x, ZhaoHong. Modeling and analysis of active-benign worms and hybrid-benign worms [J]. Journal of Computer Research and Development, 2007, 44(6): 958-964

[11]

RhodesC, AndersonR. Contact rate calculation for a basic epidemic model [J]. Mathematical Biosciences, 2008, 216(1): 56-62

[12]

HadallerD, KeshavS, BrechtT, AgarwalS. Vehicular opportunistic communication under the microscope [C]. Proceedings of the 5th International Conference on Mobile Systems, Applications and Services, 2007New York, USAACM206-219

[13]

AdrianE, EtienneS, ChristianE, FranklinM. Estimating the performance of intelligent transport systems wireless services for multimodal logistics applications [J]. Expert Systems with Applications, 2012, 39(4): 3939-3949

[14]

AmadeoM, CampoloC, MolinaroA. Enhancing IEEE 802.11p/WAVE to provide infotainment applications in VANETs [J]. Ad Hoc Networks, 2012, 10(2): 253-269

[15]

HongW, ZhongZ-d, XiongL, AiB, HeRuisi. Study on the shadow fading characteristic in viaduct scenario of the High-speed Railway [C]. Proceedings of the 2011 6th International ICST Conference on Communications and Networking, 2011Harbin, ChinaIEEE1216-1210

[16]

SinghJ P, BambosN, SrinivasanB, ClawinD. Wireless LAN performance under varied stress conditions in vehicular traffic scenarios [C]. Proceedings of IEEE Vehicular Technology Conference’2002, 2002Vancouver, CanadaIEEE Press743-747

[17]

BerettaE, TakeuchiY. Convergence results in SIR epidemic models with varying population sizes [J]. Nonlinear Analysis: Theory, Methods & Applications, 1997, 28(12): 1909-1921

[18]

ZouC, GaoL, GongW, TowsleyD. Monitoring and early warning for Internet worms [C]. Proceedings of the 10th ACM Conference on Computer and Communications Security, 2003Washington DC, USAACM190-199

[19]

BaileyM, CookeE, JahanianF, NazarioJ, WatsonD. The internet motion sensor: A distributed blackhole monitoring system [C]. Proceedings of the 12th ISOC symposium on network and distributed systems security, 2005San Diego, USAIEEE Computer Society Press167-179

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