Ziaur Rahman
ABSTRACT
Since the beginning of this decade, several incidents report that false data injection attacks targeting intelligent connected vehicles cause huge industrial damage and loss of lives. Data Theft, Flooding, Fuzzing, Hijacking, Malware Spoofing and Advanced Persistent Threats have been immensely growing attack that leads to end-user conflict by abolishing trust on autonomous vehicle. Looking after those sensitive data that contributes to measure the localisation factors of the vehicle, conventional centralised techniques can be misused to update the legitimate vehicular status maliciously. As investigated, the existing centralized false data detection approach based on state and likelihood estimation has a reprehensible trade-off in terms of accuracy, trust, cost, and efficiency. Blockchain with Fuzzy-logic Intelligence has shown its potential to solve localisation issues, trust and false data detection challenges encountered by today’s autonomous vehicular system. The proposed Blockchain-based fuzzy solution demonstrates a novel false data detection and reputation preservation technique. The illustrated proposed model filters false and anomalous data based on the vehicles’ rules and behaviours. Besides improving the detection accuracy and eliminating the single point of failure, the contributions include appropriating fuzzy AI functions within the Road-side Unit node before authorizing status data by a Blockchain network. Finally, thorough experimental evaluation validates the effectiveness of the proposed model.
- M. Aazam, S. Zeadally, and K. A. Harras. 2018. Deploying Fog Computing in Industrial Internet of Things and Industry 4.0. IEEE Transactions on Industrial Informatics 14, 10 (2018), 4674–4682. https://doi.org/10.1109/TII.2018.2855198Google Scholar
Cross Ref
- N. Z. Aitzhan and D. Svetinovic. 2018. Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams. IEEE Transactions on Dependable and Secure Computing 15, 5 (2018), 840–852. https://doi.org/10.1109/TDSC.2016.2616861Google ScholarCross Ref
- N. Z. Aitzhan and D. Svetinovic. 2018. Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams. IEEE Transactions on Dependable and Secure Computing 15, 5 (2018), 840–852. https://doi.org/10.1109/TDSC.2016.2616861Google ScholarCross Ref
- Adnan Anwar, Abdun Naser Mahmood, and Mark Pickering. 2017. Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements. J. Comput. System Sci. 83, 1 (2017), 58 – 72.Google ScholarCross Ref
- Zoleikha Abdollahi Biron, Satadru Dey, and Pierluigi Pisu. 2018. Real-time detection and estimation of denial of service attack in connected vehicle systems. IEEE Transactions on Intelligent Transportation Systems 19, 12 (2018), 3893–3902.Google ScholarDigital Library
- Eunsang Cho, Jeongnyeo Kim, Minkyung Park, Hyeonmin Lee, Chorom Hamm, Soobin Park, Sungmin Sohn, Minhyeok Kang, and Ted Taekyoung Kwon. 2020. TwinPeaks: An approach for certificateless public key distribution for the internet and internet of things. Computer Networks 175 (2020), 107268. https://doi.org/10.1016/j.comnet.2020.107268Google ScholarCross Ref
- Kakan C Dey, Li Yan, Xujie Wang, Yue Wang, Haiying Shen, Mashrur Chowdhury, Lei Yu, Chenxi Qiu, and Vivekgautham Soundararaj. 2015. A review of communication, driver characteristics, and controls aspects of cooperative adaptive cruise control (CACC). IEEE Transactions on Intelligent Transportation Systems 17, 2 (2015), 491–509.Google ScholarDigital Library
- Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, and Ji Wang. 2018. Untangling blockchain: A data processing view of blockchain systems. IEEE Transactions on Knowledge and Data Engineering 30, 7 (2018), 1366–1385.Google ScholarCross Ref
- Zubair Baig Erwin Adi, Adnan Anwar and Sherali Zeadalli. 2020. Machine learning and data analytics for the IoT. Neural Computing and Applications 32 (2020).Google Scholar
- Vincent Gramoli. 2020. From blockchain consensus back to Byzantine consensus. Future Generation Computer Systems 107 (2020), 760–769. https://doi.org/10.1016/j.future.2017.09.023Google ScholarDigital Library
- D. Huang, X. Ma, and S. Zhang. 2020. Performance Analysis of the Raft Consensus Algorithm for Private Blockchains. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, 1 (2020), 172–181. https://doi.org/10.1109/TSMC.2019.2895471Google ScholarCross Ref
- Hyo Jin Jo, In Seok Kim, and Dong Hoon Lee. 2015. Efficient and privacy-preserving metering protocols for smart grid systems. IEEE Transactions on Smart Grid 7, 3 (2015), 1732–1742.Google ScholarCross Ref
- Zhiyang Ju, Hui Zhang, and Ying Tan. 2020. Distributed deception attack detection in platoon-based connected vehicle systems. IEEE transactions on vehicular technology 69, 5 (2020), 4609–4620.Google ScholarCross Ref
- SAHU AMIYA Kumar, Sharma Suraj, and Puthal Deepak. 2020. Lightweight Multi-Party Authentication and Key-Agreement Protocol in IoT Based e-Healthcare Service. ACM Trans. Multimedia Comput. Commun. Appl. 0, ja (2020). https://doi.org/10.1145/3398039Google ScholarDigital Library
- Beibei Li, Rongxing Lu, Wei Wang, and Kim-Kwang Raymond Choo. 2017. Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. J. Parallel and Distrib. Comput. 103 (2017), 32–41.Google ScholarDigital Library
- R. Li, T. Song, B. Mei, H. Li, X. Cheng, and L. Sun. 2019. Blockchain for Large-Scale Internet of Things Data Storage and Protection. IEEE Transactions on Services Computing 12, 5 (2019), 762–771. https://doi.org/10.1109/TSC.2018.2853167Google ScholarCross Ref
- Shang Li and Xiaodong Wang. 2015. Cooperative change detection for voltage quality monitoring in smart grids. IEEE Transactions on Information Forensics and Security 11, 1 (2015), 86–99.Google ScholarDigital Library
- Wenjia Li and Houbing Song. 2015. ART: An attack-resistant trust management scheme for securing vehicular ad hoc networks. IEEE transactions on intelligent transportation systems 17, 4 (2015), 960–969.Google Scholar
- Xin Li, Minmei Wang, Huazhe Wang, Ye Yu, and Chen Qian. 2019. Toward Secure and Efficient Communication for the Internet of Things. IEEE/ACM Trans. Netw. 27, 2 (2019), 621–634. https://doi.org/10.1109/TNET.2019.2893249Google ScholarDigital Library
- Gaoqi Liang, Junhua Zhao, Fengji Luo, Steven R Weller, and Zhao Yang Dong. 2016. A review of false data injection attacks against modern power systems. IEEE Transactions on Smart Grid 8, 4 (2016), 1630–1638.Google ScholarCross Ref
- J. M. Mendel and D. Wu. 2017. Critique of “A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems”. IEEE Transactions on Fuzzy Systems 25, 3 (2017), 725–727. https://doi.org/10.1109/TFUZZ.2017.2648882Google ScholarDigital Library
- Esther Mengelkamp, Benedikt Notheisen, Carolin Beer, David Dauer, and Christof Weinhardt. 2018. A blockchain-based smart grid: towards sustainable local energy markets. Computer Science-Research and Development 33, 1-2 (2018), 207–214.Google ScholarCross Ref
- Michael Mylrea and Sri Nikhil Gupta Gourisetti. 2017. Blockchain for smart grid resilience: Exchanging distributed energy at speed, scale and security. In 2017 Resilience Week (RWS). IEEE, 18–23.Google Scholar
- John A Nix. 2016. Secure PKI communications for machine-to-machine modules, including key derivation by modules and authenticating public keys. https://patentimages.storage.googleapis.com/24/4d/11/4e1186842d8b5a/US9998280.pdf US Patent 9,288,059.Google Scholar
- Jonathan Petit and Steven E Shladover. 2014. Potential cyberattacks on automated vehicles. IEEE Transactions on Intelligent transportation systems 16, 2 (2014), 546–556.Google Scholar
- Ziaur Rahman, Ibrahim Khalil, Xun Yi, and Mohammed Atiquzzaman. 2021. Blockchain-Based Security Framework for a Critical Industry 4.0 Cyber-Physical System. IEEE Communications Magazine 59, 5 (2021), 128–134. https://doi.org/10.1109/MCOM.001.2000679Google ScholarCross Ref
- Ziaur Rahman, Xun Yi, and Ibrahim Khalil. 2022. Blockchain based AI-enabled Industry 4.0 CPS Protection against Advanced Persistent Threat. IEEE Internet of Things Journal (2022), 1–1. https://doi.org/10.1109/JIOT.2022.3147186Google Scholar
- Mingshun Sun, Ali Al-Hashimi, Ming Li, and Ryan Gerdes. 2020. Impacts of constrained sensing and communication based attacks on vehicular platoons. IEEE transactions on vehicular technology 69, 5 (2020), 4773–4787.Google ScholarCross Ref
- T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, and D. Sabella. 2017. On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration. IEEE Communications Surveys Tutorials 19, 3 (2017), 1657–1681. https://doi.org/10.1109/COMST.2017.2705720Google ScholarDigital Library
- N. B. Truong, K. Sun, G. M. Lee, and Y. Guo. 2020. GDPR-Compliant Personal Data Management: A Blockchain-Based Solution. IEEE Transactions on Information Forensics and Security 15 (2020), 1746–1761. https://doi.org/10.1109/TIFS.2019.2948287Google ScholarDigital Library
- F. Tschorsch and B. Scheuermann. 2016. Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies. IEEE Communications Surveys Tutorials 18, 3 (2016), 2084–2123. https://doi.org/10.1109/COMST.2016.2535718Google ScholarDigital Library
- L. Wang. 2017. A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems. IEEE Transactions on Fuzzy Systems 25, 3 (2017), 693–706. https://doi.org/10.1109/TFUZZ.2016.2543746Google ScholarDigital Library
- S. Wang, L. Ouyang, Y. Yuan, X. Ni, X. Han, and F. Wang. 2019. Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, 11 (2019), 2266–2277.Google ScholarCross Ref
- S. Wang, A. F. Taha, J. Wang, K. Kvaternik, and A. Hahn. 2019. Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, 8 (2019), 1612–1623. https://doi.org/10.1109/TSMC.2019.2916565Google ScholarCross Ref
- M. Wollschlaeger, T. Sauter, and J. Jasperneite. 2017. The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0. IEEE Industrial Electronics Magazine 11, 1 (2017), 17–27. https://doi.org/10.1109/MIE.2017.2649104Google Scholar
- Guomin Yang and Chik How Tan. 2011. Certificateless cryptography with KGC trust level 3. Theoretical Computer Science 412, 39 (2011), 5446 – 5457. https://doi.org/10.1016/j.tcs.2011.06.015Google ScholarCross Ref
- Mahdi Zamani, Mahnush Movahedi, and Mariana Raykova. 2018. RapidChain: Scaling Blockchain via Full Sharding. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, 931–948. https://doi.org/10.1145/3243734.3243853Google ScholarDigital Library
- Chunheng Zhao, Jasprit Singh Gill, Pierluigi Pisu, and Gurcan Comert. 2021. Detection of false data injection attack in connected and automated vehicles via cloud-based sandboxing. IEEE Transactions on Intelligent Transportation Systems 23, 7 (2021), 9078–9088.Google ScholarDigital Library
Index Terms
- Blockchain-Based and Fuzzy Logic-Enabled False Data Discovery for the Intelligent Autonomous Vehicular System
Recommendations
The Exploration of Autonomous Vehicle Driving Styles: Preferred Longitudinal, Lateral, and Vertical Accelerations
Automotive'UI 16: Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsThis paper describes a new approach in exploring preferred driving styles for autonomous vehicles through simulation of autonomous driving in real road conditions. A Wizard experiment with an equipped car was conducted to investigate the preferences of ...
Fuzzy Logic Ticket Rate Predictor for Congestion Control in Vehicular Networks
Cooperative vehicular systems are currently being investigated to design innovative intelligent transportation systems (ITS) solutions for road traffic management and safety. This paper proposes a preventive congestion control mechanism applied at ...
Detection of false data attacks in sensor networks based on the APIT location algorithm
In order to overcome the big error problem of false data location in traditional false data attack detection methods for sensor networks, this paper proposes a new false data attack detection method for sensor networks based on the APIT location ...
Comments