MBID: Micro-Blockchain-Based Geographical Dynamic Intrusion Detection for V2X

Vehicle-to-everything (V2X) aims to make transportation system more intelligent through linking everything with the moving vehicles, but it brings geographical dynamic intrusions. However, existing intrusion detection systems (IDSs) of vehicles just deploy the preset statics strategies. As a novel security technology, blockchain can realize decentralized tamper- resistance. However, it has not been used for IDSs because of its rigid structure. In this article, we propose Micro-Blockchain based geographical dynamic Intrusion Detection, MBID, for V2X. A novel nested micro-blockchain structure is proposed, where each micro-blockchain deployed in a small region can construct local intrusion detection strategies for vehicles with tamper- resistance. When a vehicle moves to another region, spatial-temporal dynamic IDSs strategies are constructed through the proposed repeatedly nested scheme for micro-blockchains. Moreover, the control plane is proposed to dynamically configure IDSs strategies into the micro-blockchain. Simulation results show the accuracy of MBID.

[1]  Rafik Braham,et al.  Intrusion Threats and Security Solutions for Autonomous Vehicle Networks , 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).

[2]  Kehe Wu,et al.  A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks , 2018, IEEE Access.

[3]  Sanjay E. Sarma,et al.  A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas , 2017, IEEE Transactions on Intelligent Transportation Systems.

[4]  Jun Wu,et al.  NLES: A Novel Lifetime Extension Scheme for Safety-Critical Cyber-Physical Systems Using SDN and NFV , 2019, IEEE Internet of Things Journal.

[5]  Zhetao Li,et al.  Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[6]  Naveen K. Chilamkurti,et al.  Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications , 2018, IEEE Communications Magazine.

[7]  Yunpeng Wang,et al.  Comparative Performance Evaluation of Intrusion Detection Methods for In-Vehicle Networks , 2018, IEEE Access.

[8]  Xiangliang Zhang,et al.  CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles , 2018, IEEE Transactions on Intelligent Transportation Systems.

[9]  Cheng Zhang,et al.  Task migration for mobile edge computing using deep reinforcement learning , 2019, Future Gener. Comput. Syst..

[10]  Georgia Sakellari,et al.  Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning , 2018, IEEE Access.

[11]  Muttukrishnan Rajarajan,et al.  Host-Based Intrusion Detection for VANETs: A Statistical Approach to Rogue Node Detection , 2016, IEEE Transactions on Vehicular Technology.

[12]  Chen-Ching Liu,et al.  Intelligent Electronic Devices With Collaborative Intrusion Detection Systems , 2019, IEEE Transactions on Smart Grid.

[13]  Hong Liu,et al.  Blockchain-Enabled Security in Electric Vehicles Cloud and Edge Computing , 2018, IEEE Network.