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Multi feature behavior approximation model based efficient botnet detection to mitigate financial frauds

  • M. D. Amala DhayaEmail author
  • R. Ravi
Original Research
  • 21 Downloads

Abstract

Money laundering and other financial frauds are increasing day by day and the financial industries face various challenges from them. They construct botnets to generate such fraudulent attacks towards financial sectors. To mitigate such threats and detect the presence of botnet, different solutions have been arrived earlier. But they struggle to achieve higher performance in detecting such botnet and restrict them from fraudulent transactions. To improve the performance, a novel multi feature behavior approximation algorithm has been presented in this article. The multi feature behavior approximation algorithm monitors each transaction performed by different users, their behavior in accessing service, the status of service access and so on. This botnet detection scheme monitors the behaviors of users and intermediate nodes involved in each transaction. Using the trace, the method performs behavior approximation in two ways like source orient and intermediate orient. In both the scheme, the method considers the frequency of transactions, their status, completion, the intermediate nodes involved and their reputation. Using all these, multi feature trust measure (MFTS) is estimated. Based on the value of MFTS, the method detects the presence of botnet and mitigates them by eliminating the node according to the backward trust score. The transaction has been accepted only when the backward trust score is high enough. The proposed algorithm improves the performance of botnet detection and reduces the frequency of money laundering.

Keywords

Botnet attack Money laundering Financial frauds Behavior analysis MFTS BTS 

Notes

Acknowledgements

Financial support obtained from the All India Council for Technical Education (AICTE) under Research Promotion Scheme (RPS), Sanction order no: F.No 8.9/RIFD/RPS/Policy-1/2017-18 coordinated by Anna University Recognized Research Centre, Department of Computer Science and Engineering, Francis Xavier Engineering College, Vannarpettai, Tirunelveli 627003, Tamilnadu, India.

References

  1. Alauthaman M (2018) A P2P botnet detection scheme based on decision tree and adaptive multilayer neural networks. Neural Comput Appl 29(11):991–1004CrossRefGoogle Scholar
  2. Beiknejad H (2018) P2P botnet detection based on traffic behavior analysis and classification, (IJOCIT). Comput Secur 6(1):1–12Google Scholar
  3. Chen R (2017) An effective conversation-based botnet detection method. Math Probl Eng 2017:1Google Scholar
  4. Chen S, Chen Y, Tzeng W (2018) Effective botnet detection through neural networks on convolutional features. In: IEEE (TrustCom/BigDataSE), pp 372–378Google Scholar
  5. Chowdhury S (2017a) Botnet detection using graph-based feature clustering. J Big Data 4(1):1MathSciNetCrossRefGoogle Scholar
  6. Chowdhury S (2017b) Botnet detection using graph-based feature clustering. J Big Data 2017:4Google Scholar
  7. Dietz C (2018) IoT-botnet detection and isolation by access routers. In: IEEE (NOF), pp 88–95Google Scholar
  8. Gayatri D (2018) An intelligent network traffic based botnet detection system. TROI 5(4):6Google Scholar
  9. Kaur N, Singh M (2016) Botnet and botnet detection techniques in cyber realm. In: IEEE (ICICT), pp 1–7Google Scholar
  10. Lange T (2019) On security threat of botnets to cyber systems. In: IEEE, sixth, international conference on signal processing and integrated networksGoogle Scholar
  11. Mathur L (2018) Botnet detection via mining of network traffic flow. Procedia Comput Sci 132:1668–1677CrossRefGoogle Scholar
  12. Miller S, Busby-Earle C (2016) The role of machine learning in botnet detection. In: IEEE (ICITST), pp 359–364Google Scholar
  13. Nagarajan P, Di Troia F (2018) Autocorrelation analysis of financial botnet traffic. In Proceedings of the 4th international conference on information systems security and privacy (ICISSP 2018), pp 599–606Google Scholar
  14. Riccardi M (2010) A framework for financial botnet analysis. In: IEEE, Conference on ECrimeGoogle Scholar
  15. Saudi NHM (2017) Revealing the feature influence in HTTP Botnet detection. Int J Commun Netw Inf Secur 9:2Google Scholar
  16. Su S-C (2018) Detecting P2P botnet in software defined networks. Secur Commun Netw 2018:13Google Scholar
  17. Yang Z (2019) P2P botnet detection based on nodes correlation by the mahalanobis distance. MDPI 10(160):1–16Google Scholar
  18. Yin C, Zhu Y, Liu S, Fei J, Zhang H (2018) An enhancing framework for botnet detection using generative adversarial networks. In: IEEE (ICAIBD), pp 228–234Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringLoyola Institute of Technology and ScienceThovalaiIndia
  2. 2.Department of Computer Science and EngineeringFrancis Xavier Engineering CollegeTirunelveliIndia

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