Anomaly detection in Smart Traffic Light system using blockchain: Securing through proof of stake and machine learning
Abstract
The Smart Traffic Light system plays a crucial role in smart cities, contributing significantly to enhancing the overall urban living standards, supporting sustainable practices, assuring public safety, and optimizing operational efficiency. Smart lighting systems leverage advanced technology such as the Internet of Things (IoT), data analytics, and networking to construct a complex and flexible lighting infrastructure. Anomalies within the context of a Smart Light system’s data pertain to patterns, events, or behaviors that are unexpected or uncommon, displaying a considerable deviation from the system’s typical operational state. The distributed and tamper-resistant ledger of blockchain technology renders it well-suited for maintaining an open and immutable record of data and events pertaining to smart lighting systems. The integration of blockchain technology and anomaly detection techniques enables the establishment of a resilient and reliable smart lighting system. The primary objective of this study is to investigate the application of the Isolation Forest algorithm for anomaly detection and its enhancement through the implementation of Proof of Stake for enhanced security measures. The Isolation Forest algorithm is utilized for anomaly prediction and evaluation of accuracy and precision. This is achieved by employing synthetic ground truth labels. Subsequently, the non-anomalous data is incorporated into the system as blocks using blockchain technology. The experimentation involves the use of synthetic numerical data derived from a dataset accessible on Kaggle. This process is conducted in two distinct phases. The first phase focuses on the analysis of synthetic numerical data, specifically targeting the identification of anomalies, prior to the implementation of blockchain technology. In the second phase, the same technique is applied to the synthetic numerical data following the integration of blockchain technology. Various machine learning (ML) models were utilized to analyze the data, resulting in improved accuracy from 48% to 94%, as evidenced by the validation of the obtained results. The empirical results indicate that the utilization of blockchain technology in data processing leads to an improvement in accuracy. Furthermore, the random forest algorithm exhibits robust performance when integrated with blockchain technology.
Keywords
Full Text:
PDFReferences
1. Kyvelou SS, Bobolos N, Tsaligopoulos A. Exploring the Effects of “Smart City” in the Inner-City Fabric of the Mediterranean Metropolis: Towards a Bio-Cultural Sonic Diversity? Heritage. 2021, 4(2): 690–709. doi: 10.3390/heritage4020039
2. Wang C, Gu J, Sanjuán Martínez O, et al. Economic and environmental impacts of energy efficiency over smart cities and regulatory measures using a smart technological solution. Sustainable Energy Technologies and Assessments. 2021, 47: 101422. doi: 10.1016/j.seta.2021.101422
3. Paiva S, Ahad M, Tripathi G, et al. Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges. Sensors. 2021, 21(6): 2143. doi: 10.3390/s21062143
4. Kishore K, Sharma S. Information Security & Privacy in Real life-Threats & Mitigations: A Review. International Journal of Computer Science and Technology. 2013, 4(3): 38–41.
5. Sarker IH. Smart City Data Science: Towards data-driven smart cities with open research issues. Internet of Things. 2022, 19: 100528. doi: 10.1016/j.iot.2022.100528
6. Shamneesh S, Manoj M, Keshav K. Node-Level Self-Adaptive Network Path Restructuring Technique for Internet of Things (IoT). Intelligent Communication, Control and Devices. Published online August 28, 2019: 453–461. doi: 10.1007/978-981-13-8618-3_48
7. Wan Y, Xu K, Wang F, et al. Characterizing and Mining Traffic Patterns of IoT Devices in Edge Networks. IEEE Transactions on Network Science and Engineering. 2021, 8(1): 89–101. doi: 10.1109/tnse.2020.3026961
8. Wang Q, Chen L, Wang Q, et al. Anomaly-Aware Network Traffic Estimation via Outlier-Robust Tensor Completion. IEEE Transactions on Network and Service Management. 2020, 17(4): 2677–2689. doi: 10.1109/tnsm.2020.3024932
9. Sharma S, Manuja M, Kishore K. Vulnerabilities, Attacks and their Mitigation: An Implementation on Internet of Things (IoT). International Journal of Innovative Technology and Exploring Engineering. 2019, 8(10): 146–150. doi: 10.35940/ijitee.f3761.0881019
10. Alaslani M, Nawab F, Shihada B. Blockchain in IoT Systems: End-to-End Delay Evaluation. IEEE Internet of Things Journal. 2019, 6(5): 8332–8344. doi: 10.1109/jiot.2019.2917226
11. Sharma C, Sharma S, Sakshi. Latent DIRICHLET allocation (LDA) based information modelling on blockchain technology: A review of trends and research patterns used in integration. Multimedia Tools and Applications. 2022, 81(25): 36805–36831. doi: 10.1007/s11042-022-13500-z
12. Gupta M, Sharma S, Sakshi, et al. Security and Privacy Issues in Blockchained IoT. Blockchain Technology. Published online February 24, 2022: 27–56. doi: 10.1201/9781003138082-3
13. Angiulli F, Fassetti F, Serrao C. Anomaly detection with correlation laws. Data & Knowledge Engineering. 2023, 145: 102181. doi: 10.1016/j.datak.2023.102181
14. Schäffer M, di Angelo M, Salzer G. Performance and Scalability of Private Ethereum Blockchains. Lecture Notes in Business Information Processing. Published online 2019: 103–118. doi: 10.1007/978-3-030-30429-4_8
15. Bhutta MNM, Khwaja AA, Nadeem A, et al. A Survey on Blockchain Technology: Evolution, Architecture and Security. IEEE Access. 2021, 9: 61048–61073. doi: 10.1109/access.2021.3072849
16. Meisami S, Meisami S, Yousefi M, et al. Combining Blockchain and IoT for Decentralized Healthcare Data Management. International Journal on Cryptography and Information Security. 2023, 13(1): 35–50. doi: 10.5121/ijcis.2023.13102
17. Chen HY, Sharma K, Sharma C, et al. Integrating explainable artificial intelligence and blockchain to smart agriculture: Research prospects for decision making and improved security. Smart Agricultural Technology. 2023, 6: 100350. doi: 10.1016/j.atech.2023.100350
18. Kishore K, Sharma S. Evolution of wireless sensor networks as the framework of Internet of Things—A review. International Journal of Emerging Research in Management &Technology. 2016, 5.
19. Sharma K, Sharma C, Sharma S, et al. Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling. Journal of Sensors. 2022, 2022: 1–19. doi: 10.1155/2022/5442865
20. Bali S, Sharma S. Anticipating Legal Issues Associated with the Cyber Security and Privacy of Automated Driving Systems in India. Autonomous Driving and Advanced Driver-Assistance Systems (ADAS). Published online November 12, 2021: 389–400. doi: 10.1201/9781003048381-20
21. Sharma N, Mishra S. Integration of Blockchain Technology into Smart Cities for Information Security: Research Constituents and Need of Research. 7th International Joint Conference on Computing Sciences (ICCS-2023). 2023.
22. Singh S, Malik A, Batra I, et al. Need for Integration of Blockchain Technology in Supply Chain Management of Health Supplements. 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). Published online May 12, 2023. doi: 10.1109/icacite57410.2023.10183099
23. Sharma S, Mishra N. Horizoning recent trends in the security of smart cities: Exploratory analysis using latent semantic analysis. Journal of Intelligent & Fuzzy Systems. Published online November 7, 2023: 1–18. doi: 10.3233/jifs-235210
24. Zhao H, Zhang W, Wu X, et al. Outlier Detection and Trust based Distributed Cooperative Spectrum Sensing in Internet of Vehicles. 2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT). Published online August 2022. doi: 10.1109/ccpqt56151.2022.00027
25. Mirsky Y, Golomb T, Elovici Y. Lightweight collaborative anomaly detection for the IoT using blockchain. Journal of Parallel and Distributed Computing. 2020, 145: 75–97. doi: 10.1016/j.jpdc.2020.06.008
26. Mathew SS, Hayawi K, Dawit NA, et al. Integration of blockchain and collaborative intrusion detection for secure data transactions in industrial IoT: A survey. Cluster Computing. 2022, 25(6): 4129–4149. doi: 10.1007/s10586-022-03645-9
27. Kumar P, Kumar R, Srivastava G, et al. PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities. IEEE Transactions on Network Science and Engineering. 2021, 8(3): 2326–2341. doi: 10.1109/tnse.2021.3089435
28. Maskey SR, Badsha S, Sengupta S, et al. BITS: Blockchain based Intelligent Transportation System with Outlier Detection for Smart City. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). Published online March 2020. doi: 10.1109/percomworkshops48775.2020.9156237
29. Cook AA, Misirli G, Fan Z. Anomaly Detection for IoT Time-Series Data: A Survey. IEEE Internet of Things Journal. 2020, 7(7): 6481–6494. doi: 10.1109/jiot.2019.2958185
30. Ramapatruni S, Narayanan SN, Mittal S, et al. Anomaly Detection Models for Smart Home Security. In: Proceedings of the 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS); 27–29 May 2019; Washington, DC, USA. pp. 19-24.
31. Balogun AL, Tella A. Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression. Chemosphere. 2022, 299: 134250. doi: 10.1016/j.chemosphere.2022.134250
32. Erhan L, Ndubuaku M, Di Mauro M, et al. Smart anomaly detection in sensor systems: A multi-perspective review. Information Fusion. 2021, 67: 64–79. doi: 10.1016/j.inffus.2020.10.001
33. Thakur A, Sharma S, Sharma T. Design of Semantic Segmentation Algorithm to Classify Forged Pixels. 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT). Published online April 8, 2023. doi: 10.1109/csnt57126.2023.10134649
34. Sharma S, Kishore K. Internet of Things (IoT): A Review of Integration of Precedent, Existing & Inevitable Technologies. AGU International Journal of Engineering and Technology, 2017, 4, pp. 442–2455.
35. Ray PP. An Introduction to Dew Computing: Definition, Concept and Implications. IEEE Access. 2018, 6: 723–737. doi: 10.1109/access.2017.2775042
36. Sharma S, Manuja M, Puri D. Performance Analysis of Commodity Server with Freeware Remote Terminal Application in Homogeneous and Heterogeneous Mutli-computing Environments. Recent Innovations in Computing. Published online 2021: 3–12. doi: 10.1007/978-981-15-8297-4_1
37. Farman H, Khan Z, Jan B, et al. Smart Transportation in Developing Countries: An Internet-of-Things-Based Conceptual Framework for Traffic Control. Islam SH, ed. Wireless Communications and Mobile Computing. 2022, 2022: 1–11. doi: 10.1155/2022/8219377
38. Sharma C, Batra I, Sharma S, et al. Predicting Trends and Research Patterns of Smart Cities: A Semi-Automatic Review Using Latent Dirichlet Allocation (LDA). IEEE Access. 2022, 10: 121080-121095. doi: 10.1109/access.2022.3214310
39. Oladimeji D, Gupta K, Kose NA, et al. Smart Transportation: An Overview of Technologies and Applications. Sensors. 2023, 23(8): 3880. doi: 10.3390/s23083880
DOI: https://doi.org/10.32629/jai.v7i5.1087
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Shamneesh Sharma, Nidhi Mishra
License URL: https://creativecommons.org/licenses/by-nc/4.0/