• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 13, Pages: 592-602

Original Article

Efficient Reputation-based Cyber Attack Detection Mechanism for Big Data Environment

Received Date:12 November 2021, Accepted Date:23 February 2022, Published Date:29 March 2022


Objective: To reduce the cyber-attacks in IoT devices and also to provide security and reliable communication among the IoT devices and Edge servers, we have designed an Efficient Reputation-based Cyber-Attack Detection (ERCAD) mechanism for the Bigdata environment. Methods: This work presents an Efficient Reputation-based Cyber-Attack Detection (ERCAD) mechanism using the trust-based method. This work provides reliability and security by employing a trust-based security model and a feedback-based model. Moreover, to increase the network performance of the model which the existing system lacks, our model provides a reputation metric that classifies the malicious IoT nodes. Also, our model provides an efficient reputation cyber-attack detection communication metric for Bigdata Environment. Findings: Most of the Existing Reputation-Based Security (ERS)(1),(2),(3) models have achieved a good detection rate but have an increased failure rate. Furthermore, the ERS models have more throughput with more energy consumption and are less reliable and have not provided any QoS requirement and proper security for their models. Hence, the ERCAD model achieves a very good attack detection rate of 22.04% with a minimum detection failure rate of 33.89% for a wide range of attacks in comparison with Existing Reputation-Based Security (ERS) models. Moreover, the ERCAD improves throughput by 22.40% and with a reduction of energy consumption of 40.032% in comparison with Existing Reputation-Based Security (ERS) models and also is highly reliable by assuring QoS and security together. Novelty: The ERS models have failed to attain a better attack detection rate when the device is in the dynamic behavior. Further, are not efficient in modeling feedback reliability. Hence, our ERCAD model provides a better attack detection rate when the device is in dynamic behavior and also provides a security framework for classifying the malicious IoT nodes.

Keywords: Reliability; Security; Detection; BigData Environment; QoS requirement


  1. Awan KA, Din IU, Almogren A, Guizani M, Khan S. StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks. IEEE Access. 2020;8:21159–21177. Available from: https://dx.doi.org/10.1109/access.2020.2968948
  2. Kalkan K. SUTSEC: SDN Utilized trust based secure clustering in IoT. Computer Networks. 2020;178:107328. doi: 10.3390/fi13020048
  3. Jing X, Yan Z, Pedrycz W. Security Data Collection and Data Analytics in the Internet: A Survey. IEEE Communications Surveys & Tutorials. 2019;21(1):586–618. Available from: https://dx.doi.org/10.1109/comst.2018.2863942
  4. Du Y, Wang Z, Leung VCM. Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues. Future Internet. 2021;13(2):48. Available from: https://dx.doi.org/10.3390/fi13020048
  5. Nguyen DC, Pathirana PN, Ding M, Seneviratne A. Secure Computation Offloading in Blockchain Based IoT Networks With Deep Reinforcement Learning. IEEE Transactions on Network Science and Engineering. 2021;8(4):3192–3208. Available from: https://dx.doi.org/10.1109/tnse.2021.3106956
  6. Narayan BD, Vineetha P, Alluri BKR. Enhanced trust-based cluster head selection in wireless sensor networks. Innovations in Computer Science and Engineering. 2019:263–275. doi: 10.1007/978-981-13-7082-3_31
  7. Roman R, Lopez J, Mambo M. Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems. 2018;78:680–698. Available from: https://dx.doi.org/10.1016/j.future.2016.11.009
  8. Li W, Wu J, Cao J, Chen N, Zhang Q, Buyya R. Blockchain-based trust management in cloud computing systems: a taxonomy, review and future directions. Journal of Cloud Computing. 2021;10(1):1–34. Available from: https://dx.doi.org/10.1186/s13677-021-00247-5
  9. Najib W, Sulistyo S, Widyawan. Survey on Trust Calculation Methods in Internet of Things. Procedia Computer Science. 2019;161:1300–1307. Available from: https://dx.doi.org/10.1016/j.procs.2019.11.245
  10. Xiao L, Ding Y, Jiang D, Huang J, Wang D, Li J, et al. A Reinforcement Learning and Blockchain-Based Trust Mechanism for Edge Networks. IEEE Transactions on Communications. 2020;68(9):5460–5470. Available from: https://dx.doi.org/10.1109/tcomm.2020.2995371
  11. Gao H, Ma Z, Luo S, Wang Z. BFR-MPC: A Blockchain-Based Fair and Robust Multi-Party Computation Scheme. IEEE Access. 2019;7:110439–110450. Available from: https://dx.doi.org/10.1109/access.2019.2934147
  12. Debe M, Salah K, Rehman MHU, Svetinovic D. IoT Public Fog Nodes Reputation System: A Decentralized Solution Using Ethereum Blockchain. IEEE Access. 2019;7:178082–178093. Available from: https://dx.doi.org/10.1109/access.2019.2958355
  13. Liu H, Zhang P, Pu G, Yang T, Maharjan S, Zhang Y. Blockchain Empowered Cooperative Authentication With Data Traceability in Vehicular Edge Computing. IEEE Transactions on Vehicular Technology. 2020;69(4):4221–4232. Available from: https://dx.doi.org/10.1109/tvt.2020.2969722
  14. Zhang X, Lu R, Shao J, Zhu H, Ghorbani AA. Secure and Efficient Probabilistic Skyline Computation for Worker Selection in MCS. IEEE Internet of Things Journal. 2020;7(12):11524–11535. Available from: https://dx.doi.org/10.1109/jiot.2020.3019326
  15. Khan M. BigData Analytics Techniques to Obtain Valuable Knowledge. Indian Journal of Science and Technology. 2018;11(14):1–14. doi: 10.17485/ijst/2018/v11i14/120977
  16. Priya B, Gnanasekaran T. Optimization of Cloud Data Center using CloudSim – A methodology. 2019 3rd International Conference on Computing and Communications Technologies (ICCCT). 2019;2019:307–310. doi: 10.1109/ICCCT2.2019.8824950


© 2022 Desai & Dinesh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.