AN ENHANCED ENSEMBLE CLASSIFIERS FOR CYBERCRIMES DETECTION IN FINANCIAL NETWORKS

Abstract

The growing reliance on the use of financial networks worldwide has resulted in great concern for cyber security. Financial network is well-connected network that allows for financial transaction and free from errors, it connects people worldwide to interact, share content, and engage in discussions of mutual interest that know no geographical boundaries. Financial setups are the target for the bots due to the financial assets, sensitive data, and data collection methods. The existing security techniques such as machine learning (ML) classifiers, multi-factor authentication, and penetration tester are not proactive enough to detect attacks in financial network platform. The aim of this work is to develop cybercrimes detection model for detection of botnet attacks in financial network.