Welcome to the InfoSci Platform
Reference Hub81
Malware Threat Affecting Financial Organization Analysis Using Machine Learning Approach

Malware Threat Affecting Financial Organization Analysis Using Machine Learning Approach

Romil Rawat, Sanjaya Kumar Sarangi, Yagya Nath Rimal, P. William, Snehil Dahima, Sonali Gupta, K. Sakthidasan Sankaran
Copyright: © 2022 |Volume: 17 |Issue: 1 |Pages: 20
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799894001|DOI: 10.4018/IJITWE.304051
Cite Article Cite Article

MLA

Rawat, Romil, et al. "Malware Threat Affecting Financial Organization Analysis Using Machine Learning Approach." IJITWE vol.17, no.1 2022: pp.1-20. http://doi.org/10.4018/IJITWE.304051

APA

Rawat, R., Sarangi, S. K., Rimal, Y. N., William, P., Dahima, S., Gupta, S., & Sankaran, K. S. (2022). Malware Threat Affecting Financial Organization Analysis Using Machine Learning Approach. International Journal of Information Technology and Web Engineering (IJITWE), 17(1), 1-20. http://doi.org/10.4018/IJITWE.304051

Chicago

Rawat, Romil, et al. "Malware Threat Affecting Financial Organization Analysis Using Machine Learning Approach," International Journal of Information Technology and Web Engineering (IJITWE) 17, no.1: 1-20. http://doi.org/10.4018/IJITWE.304051

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Since 2014, Emotet has been using man-in-the-browsers (MITB) attacks to target companies in the finance industry and their clients. Its key aim is to steal victims' online money-lending records and vital credentials as they go to their banks' websites. Without analyzing network packet payload computing (PPC), IP address labels, port number traces, or protocol knowledge, the authors have used machine learning (ML) modeling to detect Emotet malware infections and recognize Emotet-related congestion flows in this work. To classify Emotet-associated flows and detect Emotet infections, the output outcome values are compared by four separate popular ML algorithms: RF (random forest), MLP (multi-layer perceptron), SMO (sequential minimal optimization technique), and the LRM (logistic regression model). The suggested classifier is then improved by determining the right hyperparameter and attribute set range. Using network packet (computation) identifiers, the random forest classifier detects Emotet-based flows with 99.9726% precision and a 92.3% true positive rating.