Intelligent Vulnerability Analysis for Connectivity and Critical-Area Integrity in IoV

The large-scale connectivity of Internet of Vehicles (IoV) is an important challenge for the Intelligent Transportation Systems (ITS). Intelligence vulnerability analysis is an excellent solution. However, existing methods for analyzing connectivity vulnerability have ignored the existence of critical areas in the system. Due to the heterogeneities of the IoV environments and services, the failure of some specific areas may seriously damage connectivity and system performance. To this end, in this paper we focus on both the dynamic connectivity and the critical-area integrity, and propose an intelligent vulnerability analysis method to effectively identify the critical area of extreme vulnerability. Specifically, we consider an intelligent analysis scenario in which roadside servers continuously learn IoV heterogeneous environment and dynamic topology, and then translate the learning results into a flexible disruption cost problem. Based on this, we utilize the spectral partitioning method to identify the minimum-cost set of topological elements whose failure not only severely damages system connectivity but also disrupts its critical areas. Furthermore, we confirm that the identified set can be used to optimize disruption cost problem, thus intelligently improving vulnerability. Simulation results show that our proposed method can effectively identify vulnerable elements and prevent significant loss in the IoV system connectivity and performance.

[1]  Hamamache Kheddouci,et al.  The Critical Node Detection Problem in networks: A survey , 2018, Comput. Sci. Rev..

[2]  Md. Arafatur Rahman,et al.  A connection probability model for communications networks under regional failures , 2018, Int. J. Crit. Infrastructure Prot..

[3]  Yao Yu,et al.  Reliable Fog-Based Crowdsourcing: A Temporal–Spatial Task Allocation Approach , 2020, IEEE Internet of Things Journal.

[4]  My T. Thai,et al.  Network Under Joint Node and Link Attacks: Vulnerability Assessment Methods and Analysis , 2015, IEEE/ACM Transactions on Networking.

[5]  Branka Vucetic,et al.  CrowdR-FBC: A Distributed Fog-Blockchains for Mobile Crowdsourcing Reputation Management , 2020, IEEE Internet of Things Journal.

[6]  Panos M. Pardalos,et al.  On New Approaches of Assessing Network Vulnerability: Hardness and Approximation , 2012, IEEE/ACM Transactions on Networking.

[7]  Peter Nijkamp,et al.  Transport resilience and vulnerability: The role of connectivity , 2015 .

[8]  Lei Guo,et al.  A Cross-Layer Security Monitoring Selection Algorithm Based on Traffic Prediction , 2018, IEEE Access.

[9]  Tie Qiu,et al.  Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach , 2021, IEEE Journal on Selected Areas in Communications.

[10]  Lav Gupta,et al.  Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things , 2019, IEEE Internet of Things Journal.

[11]  Piet Van Mieghem,et al.  Finding Critical Regions and Region-Disjoint Paths in a Network , 2015, IEEE/ACM Transactions on Networking.

[12]  Lei Xu,et al.  An Intelligent Communication Warning Vulnerability Detection Algorithm Based on IoT Technology , 2019, IEEE Access.

[13]  Muttukrishnan Rajarajan,et al.  Optimization based spectral partitioning for node criticality assessment , 2016, J. Netw. Comput. Appl..

[14]  Bin Hu,et al.  When Deep Reinforcement Learning Meets 5G-Enabled Vehicular Networks: A Distributed Offloading Framework for Traffic Big Data , 2020, IEEE Transactions on Industrial Informatics.

[15]  My T. Thai,et al.  Bound and exact methods for assessing link vulnerability in complex networks , 2014, J. Comb. Optim..

[16]  Lei Guo,et al.  Future Communications and Energy Management in the Internet of Vehicles: Toward Intelligent Energy-Harvesting , 2019, IEEE Wireless Communications.

[17]  Valdis E. Krebs,et al.  Uncloaking Terrorist Networks , 2002, First Monday.

[18]  Haibo He,et al.  Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks , 2017, IEEE Transactions on Information Forensics and Security.

[19]  Wei Peng,et al.  Assessing the vulnerability of network topologies under large-scale regional failures , 2012, Journal of Communications and Networks.

[20]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Yao Yu,et al.  Privacy Protection Scheme Based on CP-ABE in Crowdsourcing-IoT for Smart Ocean , 2020, IEEE Internet of Things Journal.

[22]  Jun Huang,et al.  Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution , 2021, IEEE Transactions on Intelligent Transportation Systems.

[23]  Martin G. Everett,et al.  A Graph-theoretic perspective on centrality , 2006, Soc. Networks.

[24]  Bin Hu,et al.  Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System , 2020, IEEE Transactions on Intelligent Transportation Systems.

[25]  Xinbing Wang,et al.  Connectivity Analysis in Wireless Networks With Correlated Mobility and Cluster Scalability , 2017, IEEE/ACM Transactions on Networking.

[26]  Bin Liu,et al.  Recognition and Vulnerability Analysis of Key Nodes in Power Grid Based on Complex Network Centrality , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[27]  Nam P. Nguyen,et al.  On the Discovery of Critical Links and Nodes for Assessing Network Vulnerability , 2013, IEEE/ACM Transactions on Networking.

[28]  Xuxun Liu,et al.  Survivability-Aware Connectivity Restoration for Partitioned Wireless Sensor Networks , 2017, IEEE Communications Letters.

[29]  M. Newman,et al.  Renormalization Group Analysis of the Small-World Network Model , 1999, cond-mat/9903357.