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Ensemble Learning Based Social Engineering Fraud Detection Module for Cryptocurrency Transactions

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Mining Intelligence and Knowledge Exploration (MIKE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13924))

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Abstract

The practical applications of blockchains can far supersede the widely known trading and cryptocurrency realm. If any service provider is looking for a consistent, immutable, and multitenancy-supported ledger, then blockchain is the promising solution. Nowadays, social engineering attacks are prevalent. And the attackers deceive cryptocurrency traders. This work investigates various ensemble learning, neural network, and machine learning algorithms for fraud detection and identifies the best decision-making algorithm. It is observed that Adaptive Boosting (AdaBoost) algorithm outperforms with an accuracy of 98.92%. Further, the fraud detection module is integrated with an application developed for cryptocurrency transactions. Before a new transaction is committed to blockchain, The fraud detection module intervenes and alerts the user. We have also designed a test bed of deployable Peer-to-Peer (P2P) network to simulate cryptocurrency transaction.

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References

  1. Soni, S., Bhushan, B.: A comprehensive survey on blockchain: working, security analysis, privacy threats and potential applications. In: 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India, pp. 922–926 (2019). https://doi.org/10.1109/ICICICT46008.2019.8993210

  2. Wang, Z., Yang, L., Wang, Q., Liu, D., Xu, Z., Liu, S.:ArtChain: blockchain-enabled platform for art marketplace. In: 2019 IEEE International Conference on Blockchain (Blockchain), vol. 2019, pp. 447–454 (2019). https://doi.org/10.1109/Blockchain.2019.00068. [3] https://scet.berkeley.edu/wp-content/uploads/BlockchainPaper.pdf

  3. Banno, R., Shudo, K.: Simulating a blockchain network with SimBlock. In: 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 3-4 (2019). https://doi.org/10.1109/BLOC.2019.8751431

  4. Tharatipyakul, A., Pongnumkul, S.: User interface of blockchain-based agri-food traceability applications: a review. IEEE Access 9, 82909–82929 (2021). https://doi.org/10.1109/ACCESS.2021.3085982

    Article  Google Scholar 

  5. Bhowmik, M., Sai Siri Chandana, T., Rudra, B.: Comparative study of machine learning algorithms for fraud detection in blockchain. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 539–541 (2021). https://doi.org/10.1109/ICCMC51019.2021.9418470

  6. Sivaranjani, S., Ananya, S., Aravinth, J., Karthika, R.: Diabetes prediction using machine learning algorithms with feature selection and dimensionality reduction. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 141–146 (2021). https://doi.org/10.1109/ICACCS51430.2021.9441935

  7. Shakhbulatov, D., Arora, A., Dong, Z., Rojas-Cessa,R.: Blockchain implementation for analysis of Carbon footprint across food supply chain. In: 2019 IEEE International Conference on Blockchain (Blockchain), pp. 546-551 (2019). https://doi.org/10.1109/Blockchain.2019.00079

  8. Wang, Z., Lin, J., Cai, Q., Wang, Q., Zha, D., Jing, J.: Blockchain-based certificate transparency and revocation transparency. IEEE Trans. Depend. Secure Comput. 19(1), 681–697 (2022). https://doi.org/10.1109/TDSC.2020.2983022

  9. Manikumar, D.V.V.S., Maheswari,B.U.: Blockchain based DDoS mitigation using machine learning techniques. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 794-800 (2020). https://doi.org/10.1109/ICIRCA48905.2020.9183092

  10. Sharma, S., Shah, K.: Exploring security threats on blockchain technology along with possible remedies. In: 2022 IEEE 7th International Conference for Convergence in Technology (I2CT), pp. 1–4 (2022). https://doi.org/10.1109/I2CT54291.2022.9825123

  11. Zhou, R., Lin,Z.: A privacy protection scheme for permissioned blockchain based on trusted execution environment. In: 2022 International Conference on Blockchain Technology and Information Security (ICBCTIS), pp. 1-4 (2022). https://doi.org/10.1109/ICBCTIS55569.2022.00012

  12. Wang, S., Ouyang, L., Yuan, Y., Ni, X., Han, X., Wang, F.-Y.: Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49(11), 2266–2277 (2019). https://doi.org/10.1109/TSMC.2019.2895123

    Article  Google Scholar 

  13. Rekha, G., Maheswari, B.U.: Raspberry Pi forensic investigation and evidence preservation using blockchain. In: 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS), Bengaluru, India, pp. 1–5 (2021). https://doi.org/10.1109/FABS52071.2021.9702622

  14. Innocent, A.A.T., Prakash,G.: Blockchain applications with privacy using efficient multiparty computation protocols. In: 2019 PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS), pp. 1-3 (2019). https://doi.org/10.1109/PhDEDITS47523.2019.8986954

  15. Shree, J., Kanimozhi, N.R., Dhanush, G.A., Haridas, A., Sravani, A., Kumar, P.: To design smart and secure purchasing system integrated with ERP using Block chain technology. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 146–150 (2020). https://doi.org/10.1109/ICCCA49541.2020.9250767

  16. Lakshminarayanan, S., Kumar, P.N., Dhanya, N.M.: Implementation of blockchain-based blood donation framework. In: Chandrabose, A., Furbach, U., Ghosh, A., Kumar, M.A. (eds.) Computational Intelligence in Data Science. ICCIDS 2020. IFIP Advances in Information and Communication Technology, vol. 578, pp. 276–290. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63467-4_22

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Computing, Bengaluru, Amrita Vishwa Vidyapeetham, Bengaluru, India

    Vishvesh Pathak & B. Uma Maheswari

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    S. Geetha

Authors
  1. Vishvesh Pathak
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  2. B. Uma Maheswari
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  3. S. Geetha
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Corresponding author

Correspondence to B. Uma Maheswari .

Editor information

Editors and Affiliations

  1. Noroff University College, Kristiansand, Norway

    Seifedine Kadry

  2. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Pathak, V., Uma Maheswari, B., Geetha, S. (2023). Ensemble Learning Based Social Engineering Fraud Detection Module for Cryptocurrency Transactions. In: Kadry, S., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2023. Lecture Notes in Computer Science(), vol 13924. Springer, Cham. https://doi.org/10.1007/978-3-031-44084-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-44084-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44083-0

  • Online ISBN: 978-3-031-44084-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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