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.
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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.
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Amala Dhaya, M.D., Ravi, R. Multi feature behavior approximation model based efficient botnet detection to mitigate financial frauds. J Ambient Intell Human Comput (2020) doi:10.1007/s12652-020-01677-w
- Botnet attack
- Money laundering
- Financial frauds
- Behavior analysis